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    Tytuł:
    On Some Properties of Support Vector Clustering
    Analiza wybranych własności taksonomicznej metody wektorów nośnych
    Autorzy:
    Trzęsiok, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/906302.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    support vector machines
    clustering
    admissibility conditions
    Opis:
    Celem referatu jest przedstawienie analizy wybranych formalnych własności taksonomicznej metody wektorów nośnych (SVC). Wyniki dotyczące nowej metody SVC zestawiono i porównano z własnościami innych znanych metod taksonomicznych. Ponieważ na ogół nie jest możliwe wskazanie, która z metod taksonomicznych daje najlepsze rezultaty, stojąc wobec konkretnego problemu, badacz musi dokonywać wyboru metody w oparciu o wiedzę dotyczącą ich własności. Zadaniem badacza jest wtedy ustalenie preferencji w zbiorze własności metod by następnie użyć ich przy doborze odpowiedniego narzędzia. Wiedza dotycząca formalnych własności metod taksonomicznych jest w referacie rozszerzona o nową- taksonomiczną metodę wektorów nośnych.
    The aim o f this paper is to analyse the relatively new clustering method - Support Vector Clustering (SVC) in terms o f fulfilling admissibility conditions. The results are compared within a group o f four other clustering methods. Since it is not possible to assess which clustering method is the "best" in general, given a specific problem the user can decide which method to apply considering some properties o f clustering methods, known as admissibility conditions. This paper expands the knowledge about the properties o f clustering methods with the properties o f SVC.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2009, 228
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Tenfold bootstrap procedure for support vector machines
    Autorzy:
    Vrigazova, Borislava
    Ivanov, Ivan
    Powiązania:
    https://bibliotekanauki.pl/articles/1839282.pdf
    Data publikacji:
    2020
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    support vector machines
    bootstrap
    cross validation
    Opis:
    Cross validation is often used to split input data into training and test set in Support vector machines. The two most commonly used cross validation versions are the tenfold and leave-one-out cross validation. Another commonly used resampling method is the random test/train split. The advantage of these methods is that they avoid overfitting in the model and perform model selection. They, however, can increase the computational time for fitting Support vector machines with the increase of the size of the dataset. In this research, we propose an alternative for fitting SVM, which we call the tenfold bootstrap for Support vector machines. This resampling procedure can significantly reduce execution time despite the big number of observations, while preserving model’s accuracy. With this finding, we propose a solution to the problem of slow execution time when fitting support vector machines on big datasets.
    Źródło:
    Computer Science; 2020, 21 (2); 241-257
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extracting Class Description from Support Vector Machines
    Profilowanie klas w metodzie wektorów nośnych
    Autorzy:
    Trzęsiok, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/904801.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    Support Vector Machines
    knowledge extraction
    model interpretability
    Opis:
    Support Vector Machines (SVMs) belong to the group of Data Mining and Machine Learning methods. SVMs are considered to be one of the best classification methods in terms of performance measure. The biggest disadvantage of SVMs is their lack of interpretability. Additional procedures can be applied that enable knowledge extraction. We present such a procedure that uses the information embedded in support vectors – the observations that define the classification function. We use recursive partitioning applied to support vectors to increase the interpretability of SVMs.
    Metoda wektorów nośnych (SVM) należy do grupy statystycznych metod uczących się. Jak większość metod z tej grupy, metoda SVM buduje modele o bardzo dobrych własnościach predykcyjnych, lecz niewielkiej interpretowalności. W celu uzyskania dodatkowej wiedzy –stosuje się dodatkowe procedury wspomagające interpretowanie wyników modelowania. W artykule przedstawiono procedurę wykorzystującą informacje zawarte w wektorach nośnych – obserwacjach istotnie wpływających na postać wyznaczonej funkcji dyskryminującej. Intepretowalność modelu końcowego uzyskano dzięki zastosowaniu modelu rekurencyjnego podziału do dyskryminacji wyznaczonych wektorów nośnych.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2013, 286
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Clustering Methods Applied to Reduce the Training Sample Size in Support Vector Machines
    Wykorzystanie metod taksonomicznych do redukcji liczebności zbioru uczącego w metodzie wektorów nośnych
    Autorzy:
    Trzęsiok, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/905051.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    support vector machines
    K-medoids
    machine learning
    Opis:
    Support vector machines belong to the group of methods of supervised learning. They generate non-linear models with good generalization abilities. The core of SVMs algorithm is the quadratic program which is solved for obtaining the optimal separating hyperplane. Because finding the solution of this quadratic program is computationally expensive, SVMs are not feasible for very large data sets. As a solution Wang, Wu and Zhang (2005) suggested to combine the AT-means clustering technique with SVMs to reduce the number of support vectors. The paper presents a common approach using K-medoids and compares it with the original SVMs.
    Metoda wektorów nośnych jest metodą dyskryminacji generującą nieliniowe modele o dużym stopniu uogólnienia (małych błędach klasyfikacji na zbiorach testowych). Jednak ze względu na dużą złożoność obliczeniową, związaną z koniecznością rozwiązania zadania optymalizacji wypukłej, które jest podstawowym elementem algorytmu metody, stosowanie metody, szczególnie w przypadku zbiorów uczących o dużej liczebności, nie zawsze jest możliwe. Złożoność obliczeniowa algorytmu metody wektorów nośnych zależy przede wszystkim od liczby obserwacji w zbiorze uczącym. Jako rozwiązanie tego problemu Wang, Wu i Zhang zaproponowali pogrupowanie danych ze zbioru uczącego za pomocą taksonomicznej metody AT-średnich i zastosowanie metody wektorów nośnych na dużo mniej licznym zbiorze środków ciężkości tak otrzymanych klas. W artykule przedstawiona została ocena analogicznego podejścia, wykorzystującego do grupowania metodę K-medoidów oraz porównanie z oryginalną metodą wektorów nośnych.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 2009, 225
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adaptation of Evolutionary Algorithms for Decision Making on Building Construction Engineering (TSP Problem)
    Autorzy:
    Wazirali, R. A.
    Alzughaibi, A. D.
    Chaczko, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/226730.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    TSP
    genetic algorithms
    GA
    support vector machines
    SVM
    Opis:
    The report revolve on building construction engineering and management, in which there are a lot of requirements such as well supervision and accuracy and being in position to forecast uncertainties that may arise and mechanisms to solve them. It also focuses on the way the building and construction can minimise the cost of building and wastages of materials. The project will be based of heuristic methods of Artificial Intelligence (AI). There are various evolution methods, but report focus on two experiments Pattern Recognition and Travelling Salesman Problem (TSP). The Pattern Recognition focuses Evolutionary Support Vector Machine Inference System for Construction Management. The construction is very dynamic are has a lot of uncertainties, no exact data this implies that the inference should change according to the environment so that it can fit the reality, therefore there a need of Support Vector Machine Inference System to solve these problems. TSP focus on reducing cost of building construction engineering and also reduces material wastages, through its principals of finding the minimum cost path of the salesman.
    Źródło:
    International Journal of Electronics and Telecommunications; 2014, 60, 1; 125-128
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Induction motor fault classification via entropy and column correlation features of 2D represented vibration data
    Autorzy:
    Basaran, Murat
    Fidan, Mehmet
    Powiązania:
    https://bibliotekanauki.pl/articles/1841827.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    entropy
    fault diagnosis
    support vector machines
    wavelet transforms
    Opis:
    Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 1; 132-142
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of Compounds Activity Concept Learned by SVM Using Robust Jaccard Based Low-dimensional Embedding
    Autorzy:
    Jastrzębski, Stanisław
    Czarnecki, Wojciech Marian
    Powiązania:
    https://bibliotekanauki.pl/articles/1373687.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
    Tematy:
    Support Vector Machines
    Locally Sensitive Hashing
    Jaccard similarity
    Opis:
    Support Vector Machines (SVM) with RBF kernel is one of the most successful models in machine learning based compounds biological activity prediction. Unfortunately, existing datasets are highly skewed and hard to analyze. During our research we try to answer the question how deep is activity concept modeled by SVM. We perform analysis using a model which embeds compounds’ representations in a low-dimensional real space using near neighbour search with Jaccard similarity. As a result we show that concepts learned by SVM is not much more complex than slightly richer nearest neighbours search. As an additional result, we propose a classification technique, based on Locally Sensitive ashing approximating the Jaccard similarity through minhashing technique, which performs well on 80 tested datasets (consisting of 10 proteins with 8 different representations) while in the same time allows fast classification and efficient online training.
    Źródło:
    Schedae Informaticae; 2015, 24; 9-19
    0860-0295
    2083-8476
    Pojawia się w:
    Schedae Informaticae
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods
    Autorzy:
    Ptak-Chmielewska, Aneta
    Powiązania:
    https://bibliotekanauki.pl/articles/1363615.pdf
    Data publikacji:
    2021-03-03
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    discriminant analysis
    support vector machines
    bankruptcy prediction
    SMEs
    Opis:
    The impact the last financial crisis had on the small- and medium-sized enterprises (SMEs) sector varied across countries, affecting them on different levels and to a different extent. The economic situation in Poland during and after the financial crisis was quite stable compared to other EU member states. SMEs represent one of the most important segments of the economy of every country. Therefore, it is crucial to develop a prediction model which easily adapts to the characteristics of SMEs. Since the Altman Z-Score model was devised, numerous studies on bankruptcy prediction have been written. Most of them involve the application of traditional methods, including linear discriminant analysis (LDA), logistic regression and probit analysis. However, most recent studies in the area of bankruptcy prediction focus on more advanced methods, such as case-based reasoning, genetic algorithms and neural networks. In this paper, the effectiveness of LDA and SVM predictions were compared. A sample of SMEs was used in the empirical analysis, financial ratios were utilised and non-financial factors were taken account of. The hypothesis assuming that multidimensional discrimination was more effective was verified on the basis of the obtained results.
    Źródło:
    Statistics in Transition new series; 2021, 22, 1; 179-195
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wykrywanie uszkodzeń węzłów w modelu ramy stalowej na podstawie analizy inertancji
    Detection of defects connection between members of steel frame on the basis of FRF changes
    Autorzy:
    Ziaja, D.
    Miller, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/105271.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Rzeszowska im. Ignacego Łukasiewicza. Oficyna Wydawnicza
    Tematy:
    detekcja uszkodzeń
    SHM
    FRF
    Support Vector Machines
    SVM
    image detection
    Opis:
    W artykule przedstawiono możliwość detekcji uszkodzeń węzłów na podstawie analizy proporcji pomiędzy wytypowanymi fragmentami funkcji przejścia (FRF). W ramach zadania wykonano eksperyment na modelu laboratoryjnym dwukondygnacyjnej ramy portalowej, którą poddano testom dynamicznym i dla której określono model modalny. Funkcję przejścia odpowiadającą wybranym punktom układu potraktowano jako sygnał w dziedzinie częstotliwości. Wyznaczono odcięte środków ciężkości kwadratów sygnału wybranych fragmentów funkcji, które następnie potraktowano jako dane wejściowe w metodzie wektorów nośnych. Zaproponowane podejście umożliwia skuteczną detekcję uszkodzeń węzłów badanego modelu.
    The article presents the possibility of nodes failures detecting based on the analysis of the proportions between the selected intervals of FRF function. Within the scope of the task an experiment was performed on the laboratory model of a two-storey portal frame, which was subjected to dynamic tests and for which a modal model was defined. FRF function for selected system points was treated as a signal in the frequency domain. For the relevant fragments, the centers of gravity of the signal squares were determined, which were then used as input data in the Support Vector Machines (SVM) method. The proposed approach enables effective detection of connection damage in the tested structure.
    Źródło:
    Czasopismo Inżynierii Lądowej, Środowiska i Architektury; 2017, 64, 2/I; 247-255
    2300-5130
    2300-8903
    Pojawia się w:
    Czasopismo Inżynierii Lądowej, Środowiska i Architektury
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Anomaly detection in a cutting tool by k-means clustering and support vector machines
    Autorzy:
    Lahrache, A.
    Cocconcelli, M.
    Rubini, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/328445.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    knife diagnostics
    k-means
    hierarchical clustering
    support vector machines
    diagnostyka
    grupowanie hierarchiczne
    Opis:
    This paper concerns the analysis of experimental data, verifying the applicability of signal analysis techniques for condition monitoring of a packaging machine. In particular, the activity focuses on the cutting process that divides a continuous flow of packaging paper into single packages. The cutting process is made by a steel knife driven by a hydraulic system. Actually, the knives are frequently substituted, causing frequent stops of the machine and consequent lost production costs. The aim of this paper is to develop a diagnostic procedure to assess the wearing condition of blades, reducing the stops for maintenance. The packaging machine was provided with pressure sensor that monitors the hydraulic system driving the blade. Processing the pressure data comprises three main steps: the selection of scalar quantities that could be indicative of the condition of the knife. A clustering analysis was used to set up a threshold between unfaulted and faulted knives. Finally, a Support Vector Machine (SVM) model was applied to classify the technical condition of knife during its lifetime.
    Źródło:
    Diagnostyka; 2017, 18, 3; 21-29
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analog Circuit Fault Classification Using Improved One-Against-One Support Vector Machines
    Autorzy:
    Cui, J.
    Wang, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/220569.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    analog circuit
    fault classification
    Support Vector Machines Classifier
    fault dictionary
    kernel parameter
    Opis:
    This paper presents a novel strategy of fault classification for the analog circuit under test (CUT). The proposed classification strategy is implemented with the one-against-one Support Vector Machines Classifier (SVC), which is improved by employing a fault dictionary to accelerate the testing procedure. In our investigations, the support vectors and other relevant parameters are obtained by training the standard binary support vector machines. In addition, a technique of radial-basis-function (RBF) kernel parameter evaluation and selection is invented. This technique can find a good and proper kernel parameter for the SVC prior to the machine learning. Two typical analog circuits are demonstrated to validate the effectiveness of the proposed method.
    Źródło:
    Metrology and Measurement Systems; 2011, 18, 4; 569-582
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Predicting the default risk of companies. Comparison of credit scoring models: LOGIT vs Support Vector Machines
    Przewidywanie ryzyka kredytowego przedsiębiorstw niefinansowych. Porównanie modeli scoringowych: regresja logistyczna vs Support Vector Machine
    Autorzy:
    Nehrebecka, Natalia
    Powiązania:
    https://bibliotekanauki.pl/articles/425217.pdf
    Data publikacji:
    2018
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    Basel III
    Internal Rating Based System
    credit scoring
    Support Vector Machines
    logistic regression
    Opis:
    The aim of the article is to compare models on a train and validation sample, which will be created using logistic regression and Support Vector Machine (SVM) and will be used to assess the credit risk of non-financial enterprises. When creating models, the variables will be subjected to the transformation of the Weight of Evidence (WoE), the number of potential predictions will be reduced based on the Information Value (IV) statistics. The quality of the models will be assessed according to the most popular criteria such as GINI statistics, Kolmogorov-Smirnov (K-S) and Area Under Receiver Operating Characteristic (AUROC). Based on the results, it was found that there are significant differences between the logistic regression model of discriminatory character and the SVM for the model sample. In the case of a validation sample, logistic regression has the best prognostic capability. These analyses can be used to reduce the risk of negative effects on the financial sector.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2018, 22, 2; 54-73
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Music Performers Classification by Using Multifractal Features : A Case Study
    Autorzy:
    Reljin, N.
    Pokrajac, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/177266.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    music classification
    multifractal analysis
    support vector machines
    cross-validation
    mel-frequency cepstral coefficients
    Opis:
    In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%.
    Źródło:
    Archives of Acoustics; 2017, 42, 2; 223-233
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Solving Support Vector Machine with Many Examples
    Autorzy:
    Białoń, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/308497.pdf
    Data publikacji:
    2010
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    concept drift
    convex optimization
    data mining
    network failure detection
    stream processing
    support vector machines
    Opis:
    Various methods of dealing with linear support vector machine (SVM) problems with a large number of examples are presented and compared. The author believes that some interesting conclusions from this critical analysis applies to many new optimization problems and indicates in which direction the science of optimization will branch in the future. This direction is driven by the automatic collection of large data to be analyzed, and is most visible in telecommunications. A stream SVM approach is proposed, in which the data substantially exceeds the available fast random access memory (RAM) due to a large number of examples. Formally, the use of RAM is constant in the number of examples (though usually it depends on the dimensionality of the examples space). It builds an inexact polynomial model of the problem. Another author's approach is exact. It also uses a constant amount of RAM but also auxiliary disk files, that can be long but are smartly accessed. This approach bases on the cutting plane method, similarly as Joachims' method (which, however, relies on early finishing the optimization).
    Źródło:
    Journal of Telecommunications and Information Technology; 2010, 3; 65-70
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Symulacyjna ocena jakości zagregowanych modeli zbudowanych metodą wektorów nośnych
    Benchmarking Aggregated Support Vector Regression Models
    Autorzy:
    Trzęsiok, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/588038.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Metoda wektorów nośnych (SVM)
    Modele matematyczne
    Symulacja
    Mathematical models
    Simulation
    Support Vector Machines (SVM)
    Opis:
    Support Vector Machines (SVM) are a state-of-the-art classification method, but they are also suitable, after a special reformulation, to perform a regression task. Similarly to classification, for a nonlinear regression problem, SVMs use the kernel trick and map the input space into a high-dimensional feature space first, and then perform linear regression in the high-dimensional feature space. One can use the model ensemble approach to try to improve the prediction accuracy. The paper presents the comparison of a single SVM, aggregated SVM and other regression models (linear regression, Projection Pursuit Regression, Neural Networks, Regression Trees, Random Forest, Bagging) by the means of a mean squared test set error.
    Źródło:
    Studia Ekonomiczne; 2013, 132; 115-126
    2083-8611
    Pojawia się w:
    Studia Ekonomiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selecting Differentially Expressed Genes for Colon Tumor Classification
    Autorzy:
    Fujarewicz, K.
    Wiench, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908154.pdf
    Data publikacji:
    2003
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    medycyna
    automatyka
    colon tumor
    gene expression data
    microarrays
    support vector machines
    feature selection
    classification
    Opis:
    DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumor/normal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumor/normal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2003, 13, 3; 327-335
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images
    Autorzy:
    Taha, Lamyaa Gamal El-deen
    Ibrahim, Rania Elsayed
    Powiązania:
    https://bibliotekanauki.pl/articles/1837996.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    ensemble classifiers
    machine learning
    random forest
    maximum likelihood
    support vector machines
    backpropagation
    image classification
    Opis:
    The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
    Źródło:
    Geomatics and Environmental Engineering; 2021, 15, 4; 101-116
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines
    Autorzy:
    Cui, J.
    Shi, G.
    Gong, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/220922.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    power electronics
    fault diagnosis
    wavelet transforms
    support vector machines
    directed acyclic graph
    nearest neighbours
    Opis:
    Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.
    Źródło:
    Metrology and Measurement Systems; 2017, 24, 4; 701-720
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fetal state evaluation with fuzzy analysis of newborn attributes using CUDA architecture
    Autorzy:
    Czabański, R.
    Wróbel, J.
    Jeżewski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/333255.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    fuzzy systems
    fetal monitoring
    support vector machines
    CUDA architecture
    systemy rozmyte
    monitorowanie płodu
    architektura CUDA
    Opis:
    Cardiotocography is a biophysical method of fetal state evaluation involving the recording and analysis of the fetal heart rate (FHR). Since a proper interpretation of the signal is relatively difficult, an automatic classification is often based on computational intelligence methods. The quality of classifiers based on supervised learning algorithms depends on a proper selection of learning data. In case of the fetal state evaluation, the learning is usually based on a set of quantitative parameters of FHR signal and the corresponding reference information determined on the basis of the retrospective analysis of newborn attributes. Values of the single attribute have been used so far as a reference. As a result, a part of information on the actual neonatal outcome has always been lost. The following paper presents a method of the fuzzy reasoning leading to an evaluation of neonatal outcome as a function of three newborn attributes. The fuzzy system was used in the process of a qualitative evaluation of the fetal state based on quantitative analysis of FHR signal using a support vector machine (SVM). In order to improve computational effectiveness, the learning algorithm was implemented in Compute Unified Device Architecture (CUDA). The results of these studies confirm the effectiveness of the proposed method and indicate the possibility of practical usage of the fuzzy system in supervised learning algorithms for the qualitative evaluation of the fetal state.
    Źródło:
    Journal of Medical Informatics & Technologies; 2013, 22; 125-133
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A two-layer neural system for reduced-reference visual quality assessment
    Autorzy:
    Redi, J.
    Gastaldo, P.
    Zunino, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/91584.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    reduced-reference system
    image
    visual quality assessmen
    luminance distribution
    visual distortions
    support vector machines
    SVM
    Opis:
    Real-time assessment of visual quality can be efficiently supported by reduced-refe-rence paradigms, which require a very limited amount of information on the original signal, easily embeddable in the signal itself. In this paper, a reduced-reference system for image quality assessment is proposed, based on a small sized numerical description of images encoding the luminance distribution and its variations due to visual distortions. The assessment paradigm is implemented exploiting machine learning tools and articulates in two phases: first, a Support Vector Machines-based classifier identifies the kind of distortion affecting the image; then, the actual quality level of the distorted image is computed by a specifically trained SVM regressor. The general validity of the approach is supported by experimental validations based on subjective quality data.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 27-41
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Genetic algorithms for classifiers training sets optimisation applied to human face recognition
    Autorzy:
    Kawulok, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333826.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    maszyna wektorów nośnych
    algorytmy genetyczne
    rozpoznawanie twarzy człowieka
    support vector machines
    genetic algorithms
    human face recognition
    Opis:
    Human face recognition is a multi-stage process within which many classification problems must be solved. This is performed by learning machines which elaborate classification rules based on a given training set. Therefore, one of the most important issues is selection of a training set which would properly represent the data that will be further classified. This paper presents an approach which utilizes genetic algorithms for selecting classifiers' training sets. This approach was implemented for the Support Vector Machines which is applied in two areas of automatic human face recognition: face verification and feature vectors comparison. Effectiveness of the presented concept was confirmed with appropriate experiments which results are described in this paper.
    Źródło:
    Journal of Medical Informatics & Technologies; 2007, 11; 135-143
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic speech based emotion recognition using paralinguistics features
    Autorzy:
    Hook, J.
    Noroozi, F.
    Toygar, O.
    Anbarjafari, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/200261.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    random forests
    speech emotion recognition
    machine learning
    support vector machines
    lasy
    rozpoznawanie emocji mowy
    nauczanie maszynowe
    Opis:
    Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a?small set of features with support vector machines as the classifier is evaluated on Surrey Audio-Visual Expressed Emotion database, Berlin Database of Emotional Speech, Polish Emotional Speech database and Serbian emotional speech database. It is shown that a?set of 87 features can offer results on-par with state-of-the-art, yielding 80.21, 88.6, 75.42 and 93.41% average emotion recognition rate, respectively. In addition, an experiment is conducted to explore the significance of gender in emotion recognition using random forests. Two models, trained on the first and second database, respectively, and four speakers were used to determine the effects. It is seen that the feature set used in this work performs well for both male and female speakers, yielding approximately 27% average emotion recognition in both models. In addition, the emotions for female speakers were recognized 18% of the time in the first model and 29% in the second. A?similar effect is seen with male speakers: the first model yields 36%, the second 28% a?verage emotion recognition rate. This illustrates the relationship between the constitution of training data and emotion recognition accuracy.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 479-488
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Efficient heart disease diagnosis based on twin support vector machine
    Autorzy:
    Brik, Youcef
    Djerioui, Mohamed
    Attallah, Bilal
    Powiązania:
    https://bibliotekanauki.pl/articles/1840868.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    heart diseases
    medical data
    diagnostic
    machine learning
    twin support vector machines
    choroba serca
    diagnostyka
    uczenie maszynowe
    Opis:
    Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target (i.e. presence or absence of disease). Unlike conventional support vector machines (SVM) that finds only one optimal hyperplane for separating the data points of first class from those of second class, which causes inaccurate decision, Twin-SVM finds two non-parallel hyper-planes so that each one is closer to the first class and is as far from the second class as possible. Our experiments are conducted on real heart disease dataset and many evaluation metrics have been considered to evaluate the performance of the proposed method. Furthermore, a comparison between the proposed method and several well-known classifiers as well as the state-of-the-art methods has been performed. The obtained results proved that our proposed method based on Twin-SVM technique gives promising performances better than the state-of-the-art. This improvement can seriously reduce time, materials, and labor in healthcare services while increasing the final decision accuracy.
    Źródło:
    Diagnostyka; 2021, 22, 3; 3-11
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rough support vector machine for classification with interval and incomplete data
    Autorzy:
    Nowicki, Robert K.
    Grzanek, Konrad
    Hayashi, Yoichi
    Powiązania:
    https://bibliotekanauki.pl/articles/91559.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    support vector machines
    rough sets
    missing features
    interval data
    three–way decision
    maszyna wektorów nośnych
    dane interwałowe
    Opis:
    The paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 47-56
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A critical comparison of discriminant analysis and svm-based approaches to credit scoring
    Porównanie analizy dyskryminacyjnej i maszyn wektorów podpierających w analizie ryzyka kredytowego
    Autorzy:
    Stąpor, Katarzyna
    Powiązania:
    https://bibliotekanauki.pl/articles/588064.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Credit scoring model
    Discriminant analysis
    Support vector machines
    Analiza dyskryminacyjna
    Maszyny wektorów podpierających
    Model oceny ryzyka kredytowego
    Opis:
    Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of these models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The paper compares two methodologies for building credit scoring models: heteroscedastic discriminant analysis-based with the support vector machines. The real-world credit dataset is used for comparison.
    Modele oceny ryzyka kredytowego stanowią podstawę działalności większości instytucji finansowych, zajmujących się udzielaniem kredytów. Celem takich modeli jest ewaluacja prawdopodobieństwa zaprzestania przez kredytobiorcę spłaty udzielonego mu kredytu. W artykule dokonano porównania dwóch modeli oceny ryzyka kredytowego, które wykorzystują nowe metody statystyczne, a także metody uczenia maszynowego do ich konstrukcji: heteroscedastyczną analizę dyskryminacyjną oraz maszyny wektorów podpierających. Dla dokonania porównania tych metod wykorzystany został ogólnie dostępny, niemiecki zbiór kredytowy.
    Źródło:
    Studia Ekonomiczne; 2016, 288; 59-70
    2083-8611
    Pojawia się w:
    Studia Ekonomiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving the efficacy of automated fetal state assessment with fuzzy analysis of delivery outcome
    Autorzy:
    Czabanski, R.
    Jezewski, M.
    Horoba, K.
    Jezewski, J.
    Leski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/333655.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    fetal monitoring
    fuzzy inference
    support vector machines
    supervised learning
    monitorowanie płodu
    wnioskowanie rozmyte
    maszyna wektorów nośnych
    uczenie nadzorowane
    Opis:
    A number of methods of the qualitative assessment of fetal heart rate (FHR) signals are based on supervised learning. The classification methods based on the supervised learning require a set of training recordings accompanied by the reference interpretation. In the real data collections the class of signals related to fetal distress is usually under-represented. Too small percentage of distress patterns adversely affects the effectiveness of the automated evaluation of the fetal state. The paper presents a method of equalizing the class sizes based on the reference assessment of the fetal state with the fuzzy analysis of the newborn attributes. The supervised learning with increased number of the FHR signals, which are characterized by the highest rate of the fuzzy inference leads to significant increase of the efficacy of the qualitative assessment of the fetal state using the Lagrangian support vector machine.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 223-230
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimal classification method for smiling vs neutral facial display recognition
    Autorzy:
    Nurzyńska, K.
    Smołka, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/333381.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    local binary patterns
    support vector machines
    k-nearest neighbourhood
    template matching
    lokalne wzorce binarne
    maszyna wektorów nośnych
    dopasowanie wzorców
    Opis:
    Human face depicts what happens in the soul, therefore correct recognition of emotion on the basis of facial display is of high importance. This work concentrates on the problem of optimal classification technique selection for solving the issue of smiling versus neutral face recognition. There are compared most frequently applied classification techniques: k-nearest neighbourhood, support vector machines, and template matching. Their performance is evaluated on facial images from several image datasets, but with similar image description methods based on local binary patterns. According to the experiments results the linear support vector machine gives the most satisfactory outcomes for all conditions.
    Źródło:
    Journal of Medical Informatics & Technologies; 2014, 23; 87-94
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Profiling bell’s palsy based on House - Brackmann score
    Autorzy:
    Song, I.
    Vong, J.
    Yen, N. Y..
    Diederich, J.
    Yellowlees, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/91551.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    facial nerve
    palsy
    support vector machines
    SVMs
    Emergent Self-Organizing Map
    ESOM
    House-Brackmann score
    facial paralysis
    facial image
    Opis:
    In this study, we propose to diagnose facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 41-50
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A framework for knowledge acqusition system in perspective view of diagnostic of rotating machinery
    System pozyskiwania wiedzy z perspektywy diagnostyki maszyn wirnikowych
    Autorzy:
    Wachla, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/329226.pdf
    Data publikacji:
    2009
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    pozyskiwanie wiedzy
    diagnostyka
    maszyna wirnikowa
    baza danych
    metoda wektorów wspomagających
    knowledge acquisition
    diagnostics
    rotating machinery
    database
    support vector machines
    Opis:
    A concept of knowledge acquisition system for the needs of diagnostic of rotor machines was presented in the article. The concept was developed on assumption that knowledge would be acquired inductively through analysis of measure and simulative data. The founding of the system was considered. The architecture was particularly described and example of its application was provided, as well.
    W artykule przedstawiono koncepcję systemu pozyskiwania wiedzy dla potrzeb diagnostyki maszyn wirnikowych. Koncepcję opracowano przyjmując założenie, że wiedza będzie pozyskiwana w sposób indukcyjny poprzez analizę danych pomiarowych lub symulacyjnych. Omówiono genezę powstania systemu. Szczegółowo opisano architekturę oraz pokazano przykład zastosowania.
    Źródło:
    Diagnostyka; 2009, 1(49); 13-16
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using multi-objective affinity model for mining the rules of revisits within 72 hours for emergency department patients
    Autorzy:
    Chao-Wen, Chen
    Yuh-Wen, Chen
    Moussa, Larbani
    Tzung-Hung, Li
    Powiązania:
    https://bibliotekanauki.pl/articles/578510.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Data Mining
    Leczenie
    Macierze
    Metoda wektorów nośnych (SVM)
    Placówki służby zdrowia
    Matrix
    Medical facilities
    Medical treatment
    Support Vector Machines (SVM)
    Opis:
    When patients return to the emergency department (ED) within 72 hours after their previous ED discharge, it is generally assumed that their initial evaluation or treatment had been somehow inadequate. Mining data related to unplanned ED revisits is one method to determine whether this problem can be overcome, and to generate useful guidelines in this regard. In this study, we use the receiver operating characteristic (ROC) curve to compare the data mining model by affinity set to other well known approaches. Some scholars have validated the affinity model for its simplicity and power in handling information systems especially when showing binary consequences. In experimental results, SVM showed the best performance, with the affinity model following only slightly behind. This study demonstrated that when patients visit the ED with normotensive status or smooth breath patterns, or when the physician-patient ratio is moderate, the frequency with which patients revisit the ED is significantly higher.
    Źródło:
    Multiple Criteria Decision Making; 2015, 10; 5-31
    2084-1531
    Pojawia się w:
    Multiple Criteria Decision Making
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Pattern Recognition Methods for Detecting Voltage Sag Disturbances and Electromagnetic Interference in Smart Grids
    Autorzy:
    Yalcin, T.
    Ozdemir, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/136160.pdf
    Data publikacji:
    2016
    Wydawca:
    EEEIC International Barbara Leonowicz Szabłowska
    Tematy:
    C4.5 decision trees
    electromagnetic interference
    feature extraction
    hilbert huang transform
    power quality disturbance
    smart grids
    support vector machines
    Opis:
    Identification of system disturbances, detection of them guarantees smart grids power quality (PQ) system reliability and provides long lasting life of the power system. The key goal of this study is to find the best accuracy of identification algorithm for non-stationary, non-linear power quality disturbances such as voltage sag, electromagnetic interference in smart grids. PQube, power quality and energy monitor, was used to acquire these distortions. Ensemble Empirical Mode Decomposition is used for electromagnetic interference reduction with first intrinsic mode function. Hilbert Huang Transform is used for generating instantaneous amplitude and instantaneous frequency feature of real time voltage sag power signal. Outputs of Hilbert Huang Transform is intrinsic mode functions (IMFs), instantaneous frequency (IF), and instantaneous amplitude (IA). Characteristic features are obtained from first IMFs, IF, and IA. The six features—, the mean, standard deviation,skewness, kurtosis of both IF and IA are then calculated. These features are normalized along with the inputs classifiers. The proposed power system monitoring system is able to detect power system voltage sag disturbances and capable of recognize electromagnetic interference component. In this study based on experimental studies, Hilbert Huang Transform based pattern recognition technique was used to investigate power signal to diagnose voltage sag and in power grid. Support Vector Machines and C4.5 Decision Tree were operated and their achievements were matched for precision and CPU timing. According to the analysis, decision tree algorithm without dimensionality reduction produces the best solution.
    Źródło:
    Transactions on Environment and Electrical Engineering; 2016, 1, 3; 86-93
    2450-5730
    Pojawia się w:
    Transactions on Environment and Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market
    Autorzy:
    Ślepaczuk, Robert
    Zenkova, Maryna
    Powiązania:
    https://bibliotekanauki.pl/articles/1356913.pdf
    Data publikacji:
    2019-08-07
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    Machine learning
    support vector machines
    investment algorithm
    algorithmic trading
    strategy
    optimization
    cross-validation
    overfitting
    cryptocurrency market
    technical analysis
    meta parameters
    Opis:
    This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
    Źródło:
    Central European Economic Journal; 2018, 5, 52; 186 - 205
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Genetic Algorithm for Feature Selection in Optimisation of SVMR Model for Prediction of Yarn Tenacity
    Zastosowanie algorytmów genetycznych do selekcji cech w optymalizacji modelu maszyn wektorów nośnych dla regresji w aspekcie prognozowania właściwości wytrzymałościowych przędzy
    Autorzy:
    Abakar, K. A. A.
    Yu, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/231903.pdf
    Data publikacji:
    2013
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
    Tematy:
    genetic algorithm
    feature selection
    support vector machines for regression
    yarn properties
    algorytm genetyczny
    wybór funkcji
    maszyny wektorów nośnych dla regresji
    właściwości przędzy
    Opis:
    A proposed hybrid genetic algorithm (GA) approach for feature selection combined with support vector machines for regression (SVMR) was applied in this paper to optimise a data set of fibre properties and predict the yarn tenacity property. This hybrid approach was compared with a noisy model of SVMR that used all the data set of fibre properties as input in the prediction. The GA for feature selection was used as the preprocessing stage that aimed to find and select the best attributes or variables that most effect or are related to the prediction of yarn tenacity. The hybrid approach showed better predictive performance than the noisy model. However, the results indicated the suitability of GA for feature selection in the choice of the best fibre property attributes that give the preferred performance and high accuracy in the prediction of yarn tenacity.
    Zaproponowany system hybrydowy łączący algorytmy genetyczne z klasyfikatorem w postaci maszyny wektorów nośnych dla regresji (SVMR) został zastosowany dla zoptymalizowania zestawu danych obejmującego właściwości fizyczne włókien dla prognozowania właściwości wytrzymałościowych przędzy. W tym hybrydowym rozwiązaniu porównano zaproponowany model SVMR z modelem „zaszumionym”, w którym użyto pełny zestaw danych właściwości fizycznych włókien jako danych wejściowych w prognozowaniu. Algorytmy genetyczne w selekcji cech zostały użyte na etapie wstępnego przetwarzania, którego celem było znalezienie i wybranie najlepszych zmiennych, które najefektywniej są powiązane z przewidywaniem wytrzymałości przędzy. Hybrydowe rozwiązanie wykazało lepsze efekty przewidywania wytrzymałości przędzy w porównaniu z modelem „zaszumionym”. Jednakże wyniki badań wykazały, że do realizacji zadania polegającego na wyborze cech z selekcji najkorzystniejszych właściwości włókien bardzo przydatne są również algorytmy genetyczne, które umożliwiają uzyskanie wysokiej dokładności prognozowania wytrzymałości przędzy.
    Źródło:
    Fibres & Textiles in Eastern Europe; 2013, 6 (102); 95-99
    1230-3666
    2300-7354
    Pojawia się w:
    Fibres & Textiles in Eastern Europe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of speaker dependent and speaker independent emotion recognition
    Autorzy:
    Rybka, J.
    Janicki, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/330055.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    speech processing
    emotion recognition
    EMO-DB
    support vector machines
    artificial neural network
    przetwarzanie mowy
    rozpoznawanie emocji
    maszyna wektorów wspierających
    sztuczna sieć neuronowa
    Opis:
    This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector Machines (SVMs), were used in experiments. SVMs turned out to provide the best classification accuracy of 75.44% in the speaker dependent mode, that is, when speech samples from the same speaker were included in the training corpus. Various speaker dependent and speaker independent configurations were analyzed and compared. Emotion recognition in speaker dependent conditions usually yielded higher accuracy results than a similar but speaker independent configuration. The improvement was especially well observed if the base recognition ratio of a given speaker was low. Happiness and anger, as well as boredom and neutrality, proved to be the pairs of emotions most often confused.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 797-808
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using Multiclass SVM methods for classification of DNA microarray data
    Autorzy:
    Student, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/333907.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    metoda cząstkowych najmniejszych kwadratów
    maszyna wektorów nośnych
    Partial Least Squares PLS
    dimension reductions
    MMulticlass Support Vector Machines MSVM
    One-Versus-One OvO
    One-Versus RestOvR
    Opis:
    One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. A classifier using Multiclass SVM [4] (Support Vector Machines) is described in this article. Our classifier involves dimension reduction using Multivariate Partial Least Squares (MPLS) for classification more than two classes. We use also two methods based on binary classifications: One-Against-All [5] and One-Against-One [6]. These three methods have been tested on a data set involving 125 tumor/normal thyroid human DNA microarrays samples. There are 66 Papillary throid carcinoma, 32 follicular throid carcinoma and 27 normal tissues. The most important thing is to find small number of genes that discriminate between these three classes with good accuracy. The best genes can be selected for Q-PCR validation. Molecular markers differentiating between throid cancer and normal tissues can help in clinical diagnostics and therapy methods. For error estimation we are use the bootstrap .632 [8] technique. Major issue with bootstrap estimators is their high computational cost. That is why we use a OpenMosix with MPI (Message Passing Interface) cluster technology for this system for parallel computation space.
    Źródło:
    Journal of Medical Informatics & Technologies; 2007, 11; 197-204
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Support Vector Machines in automatic human face recognition
    Autorzy:
    Kawulok, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333790.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    automatyczne rozpoznanie twarzy
    metoda wektorów nośnych
    wykrywanie twarzy
    wybór cech
    fuzja wielometodowa
    automatic face recognition
    support vector machines
    face detection
    feature extraction
    multi-method fusion
    Opis:
    This paper presents the possibilities of applying the Support Vector Machines (SVM) in the process of automatic human face recognition. It is described how the existing methods of face recognition can be improved by the SVM. Moreover, a new approach to the multi-method fusion utilising the SVM is proposed. Usefulness of all the methods described in the paper improving the face recognition effectiveness by the SVM is confirmed by the experimental results.
    Źródło:
    Journal of Medical Informatics & Technologies; 2005, 9; 143-150
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identification of submersible pump temperature changes model using KDD methods
    Identyfikacja modelu zmian temperatury pompy głębinowej z zastosowaniem metod odkrywania wiedzy w bazach danych
    Autorzy:
    Wachla, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/327824.pdf
    Data publikacji:
    2006
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    baza danych
    wiedza
    identyfikacja systemów
    algorytm genetyczny
    metoda wektorów wspomagających
    selekcja atrybutów
    system SCADA
    database
    knowledge
    system identification
    genetic algorithm
    support vector machines
    attributes selection
    SCADA systems
    Opis:
    This paper deals with the problem of the autoregressive model identification using KDD methods. In the considered problem, the autoregressive models are applied to describe dynamics processes of various technical systems. In particular, a method of functional dependencies discovering was presented. The method was designed for exploring data sets gathered by industrial SCADA systems. For the problem of the identification of pump temperature changes model, the method was verified. For this particular reason, a set of data was used which was gathered by submersible pumping station SCADA system. The assumptions, the exemplary results of the conducted research and conclusions were presented, as well.
    W artykule poruszono problem identyfikacji modeli autoregresyjnych opisujących dynamikę obserwowanych procesów. W szczególności przedstawiono metodę odkrywania zależności funkcyjnych w zbiorach danych gromadzonych przez przemysłowe systemy SCADA. Opracowaną metodę zweryfikowano dla problemu identyfikacji modelu zmian temperatury pompy głębinowej. W tym celu zastosowano fragment danych zgromadzony przez system rejestracji danych współpracujący pompownią głębinową. Przedstawiono przyjęte założenia, fragmenty uzyskanych wyników oraz wnioski z przeprowadzonych badań.
    Źródło:
    Diagnostyka; 2006, 2(38); 41-44
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Parameter identification of ship maneuvering models using recursive least square method based on support vector machines
    Autorzy:
    Zhu, M.
    Hahn, A.
    Wen, Y.
    Bolles, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/116455.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    ship manoeuvering
    recursive least square method
    ship manoeuvering model
    ship maneuverability prediction
    Support Vector Machines (SVM)
    empirical mode decomposition (EMD)
    Computational Fluid Dynamics (CFD)
    Extended Kalman Filter (EKF)
    Opis:
    Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS), are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM), is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD) are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2017, 11, 1; 23-29
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Metody Rozpoznawania Wzorców Obrazów w Analizie Wskaźników Dermatoglificznych Zespołu Downa
    Image pattern recognition methods in analysis of dermatoglyphic indices of Downs Syndrome
    Autorzy:
    Wojtowicz, H.
    Wajs, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/152598.pdf
    Data publikacji:
    2011
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    poprawianie jakości obrazu
    filtry adaptacyjne
    rozpoznawanie wzorców
    maszyny wektorów wspierających
    diagnostyka medyczna
    image quality
    enhancement
    adaptive filters
    pattern recognition
    classification of impressions of hallucal area of sole
    support vector machines
    medical diagnostics
    Opis:
    Klasyfikacja odbitek wzorców w polu palucha na stopach jest jest jednym z zadań analizy dermatoglificznej wykonywanej przez antropologa do wykrywania wad genetycznych u noworodków. Artykuł opisuje zastosowanie metod przetwarzania obrazów i rozpoznawania wzorców do klasyfikacji obrazów odbitek wzorców w polu halukalnym stóp. Opisana została metoda klasyfikacji odbitek tych wzorców. Do poprawienia jakości obrazów zastosowano zabiegi poprawiania kontrastu obrazu, segmentacji tła oraz kontekstowej filtracji obrazu za pomocą krótkoczasowej transformaty Fouriera. Zaproponowano zastosowanie algorytmu opartego na rozkładzie piramidowym w wielu skalach do wyznaczenia kierunków pływów listewek odbitek. W artykule opisane i przedyskutowane zostały modele klasyfikatorów obrazów odbitek wzorców w polu palucha na stopach. Klasyfikatory te stanowią część automatycznego systemu diagnostycznego służącego do badań przesiewowych na obecności trisomii 21 (zespołu Downa). System wspomaga pracę antropologa poprzez automatyczne przetwarzanie i wykrywanie własności wskazujących na obecność wad genetycznych. Obrazy dermatoglifów są wstępnie przetwarzane przed procesem klasyfikacji w celu wydobycia wektorów własności analizowanych przez Maszyny Wektorów Wspierających. Funkcje jądrowe oparte na radialnych funkcjach bazowych zostały użyte w procesie indukcji wieloklasowego systemu Maszyn Wektorów Wspierających generowanego według algorytmu 'jeden przeciwko jednemu'. Badania wykonane na danych pochodzących z Collegium Medicum Uniwersytetu Jagielońskiego w Krakowie, pokazują efektywność zaproponowanego podejścia w poprawianiu jakości obrazów odbitek wzorców w polu palucha na stopach i ich klasyfikacji.
    Classification of patterns of hallucal area of sole is one of the tasks of dermatoglyphic analysis. The paper describes application of image processing and pattern recognition methods to classification of impressions of hallucal area of sole. Contrast enhancement, segmentation and contextual filtration techniques are used to enhance quality of the images. Use of an algorithm based on multi-scale pyramid decomposition of an image is proposed for ridge orientation calculation. Hallucal area pattern classifiers, which are part of an automatic system for rapid screen diagnosing of trisomy 21 (Down's Syndrome) in infants, are created and discussed. The system is a tool supporting medical decision by automatic processing of dermatoglyphic prints and detecting features indicating presence of genetic disorder. Images of dermatoglyphic prints are pre-processed before the classification stage to extract features analysed by Support Vector Machines algorithm. RBF kernel type is used in the training of SVM multi-class systems generated with one-vs-one scheme. Experiments conducted on the database of Collegium Medicum of the Jagiellonian University in Cracow show effectiveness of the proposed approach to classification of infants' fingerprints.
    Źródło:
    Pomiary Automatyka Kontrola; 2011, R. 57, nr 9, 9; 1000-1004
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-39 z 39

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