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Tytuł:
How To Construct Support Vector Machines Without Breaching Privacy
Autorzy:
Zhan, J.
Chang, L.
Matwin, S.
Powiązania:
https://bibliotekanauki.pl/articles/92993.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
privacy
security
support vector machine (SVM)
Opis:
This paper addresses the problem of data sharing among multiple parties in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively construct support vector machines using a linear, polynomial or sigmoid kernel function. To tackle this problem, we develop a secure protocol for multiple parties to conduct the desired computation. In our solution, multiple parties use homomorphic encryption and digital envelope techniques to exchange the data while keeping it private. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning support vector machines.
Źródło:
Studia Informatica : systems and information technology; 2006, 1(7); 233-244
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evaluation of electromagnetic filtration efficiency using least squares support vector model
Autorzy:
Yuceer, M.
Yildiz, Z.
Abbasov, T.
Powiązania:
https://bibliotekanauki.pl/articles/110758.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
electromagnetic filtration
disperse systems
support vector machine (SVM)
Opis:
The present study aims to apply a least squares support vector model (LS–SVM) for predicting cleaning efficiency of an electromagnetic filtration process, also called quality factor, in order to remove corrosion particles (rust) of low concentrations from water media. For this purpose, three statistical parameters: correlation coefficient, root mean squared error and mean absolute percentage error were calculated for evaluating the performance of the LS–SVM model. It was found that the developed LS–SVM can be used to predict the effectiveness of electromagnetic filtration process.
Źródło:
Physicochemical Problems of Mineral Processing; 2015, 51, 1; 173-180
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of new method of initialisation of neuro - fuzzy systems with support vector machines
Analiza nowej metody inicjalizacji systemów neuronowo – rozmytych z wykorzystaniem maszyn wektorów wspierających
Autorzy:
Simiński, K.
Powiązania:
https://bibliotekanauki.pl/articles/375675.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
support vector machine (SVM)
neuro-fuzzy systems
classification
regression
Opis:
The correspondence between support vector machines and neuro-fuzzy systems is very interesting. The full equivalence for classification and partial for regression has been formally shown. The equivalence has very interesting implication. It is a base for a new method of initialization of neurofuzzy systems, ie. for creating of fuzzy rule base. The commonly used methods are based on reversion of item: the premises of fuzzy rules split input domain into region, thus premises of fuzzy rules can be elaborated by partition of input domain. This leads to three main classes of partition of input domain. The above mentioned equivalence results in new way of creating the rule base. Now the input domain is not partitioned, but the premises of fuzzy rules are extracted from support vector. The objective of the paper is to examine the advantages and disadvantages of this new method for creation of fuzzy rule bases for neuro-fuzzy systems.
Związek pomiedzy maszynami wektorów podpierajacych i systemami neuronoworozmytymi jest bardzo interesujący. Została wykazana pełna odpowiedniość między tymi systemami dla klasyfikacji i częściowa dla regresji. Odpowiedność ta ma bardzo ważną konsekwencję. Jest podstawa do opracowania nowego sposobu tworzenia bazy reguł dla systemu neuronowo-rozmytego. Dotychczasowe metody opieraja się na podziale przestrzeni wejściowej, a następnie przekształcenia tak powstałych regionów w przesłanki rozmytych reguł. Tutaj możliwe jest przekształcanie wektorów wspierających na przesłanki reguł rozmytych. Celem artykułu jest przebadanie możliwości stosowania takiego podejścia do inicjalizacji systemów neuronowo-rozmytych. Eksperymenty wykazują dosć istotną wadę tego podejścia. W jego wyniku powstają bardzo liczne zbiory reguł rozmytych, co zupełnie przeczy idei interpretowalności wiedzy w systemach neuronowo-rozmytych. Manipulacja pewnymi parametrami umożliwia zmiejszenie liczby reguł, jednak manipulacja ta jest trudna i wymaga wielu prób. Drugą dość istotna wadą jest wyraźnie wyższy błąd wypracowywany przez systemy inicjalizowane przez SVM w porównaniu do systemów, których bazy reguł tworzone sa˛ poprzez podział przestrzeni wejściowej.
Źródło:
Theoretical and Applied Informatics; 2012, 24, 3; 243-254
1896-5334
Pojawia się w:
Theoretical and Applied Informatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine-Learning Methods for Assessing Dynamic Resistance of Existing Bridge Structures Subjected to Mining Tremors
Metody uczenia maszynowego w ocenie odporności dynamicznej istniejących obiektów mostowych poddanych wstrząsom górniczym
Autorzy:
Rusek, J.
Powiązania:
https://bibliotekanauki.pl/articles/385657.pdf
Data publikacji:
2018
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
dynamika budowli
uczenie maszynowe
sztuczne sieci neuronowe
SVM
wstrząsy górnicze
odporność dynamiczna
mosty
dynamics of structures
machine learning
Artificial Neural Networks
SVM Support Vector Machine
mining tremors
dynamic resistance
bridges
Opis:
W pracy przedstawiono wyniki badań, których celem było utworzenie modelu pozwalającego na określenie odporności istniejących obiektów mostowych na wpływy wstrząsów górniczych. Podstawą do analiz była utworzona przez autora baza danych o odporności dynamicznej żelbetowych obiektów mostowych poddanych wymuszeniu sejsmicznemu charakterystycznemu dla terenu Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). Odporność dynamiczna każdego obiektu w bazie danych została wyrażona w postaci granicznych wartości przyspieszeń drgań gruntu, jakie dana konstrukcja może przejąć bez zagrożenia bezpieczeństwa. Badania przeprowadzono, wykorzystując metodę Support Vector Machine (SVM) w ujęciu regresyjnym (SVR – Support Vector Regression) oraz sztuczne sieci neuronowe (ANN – Artificial Neural Network). Utworzone w ten sposób modele porównano w aspekcie jakości predykcji oraz uogólniania nabytej wiedzy. Pozwoliło to na wytypowanie metody najbardziej efektywnej pod względem oceny odporności dynamicznej istniejących obiektów mostów.
This paper demonstrates the results of research studies aimed at creating a model that allows to determine the resistance of existing bridge structures to the impact of mining tremors. A database (created by the author of this article) of the dynamic resistance of reinforced concrete bridge structures subjected to seismic excitations commonly occurring in the Legnica-Głogów Copper District (LGOM) formed the basis for the analysis. The dynamic resistance of each structure contained in the database was expressed as the limit values of the acceleration of ground vibrations that may be carried by a given structure without compromising its safety. The study was carried out using the Support Vector Machine (SVM) method in a Support Vector Regression (SVR) approach as well as an Artificial Neural Network (ANN). The models were compared in terms of the quality of the predictions and generalization of the acquired knowledge. This allows to select the most-effective method in evaluating the dynamic resistance of existing bridge structures.
Źródło:
Geomatics and Environmental Engineering; 2018, 12, 1; 109-120
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast near-infrared palmprint recognition using nonnegative matrix factorization extreme learning machine
Autorzy:
Xu, X.
Zhang, X.
Lu, L.
Deng, W.
Zuo, K
Powiązania:
https://bibliotekanauki.pl/articles/173572.pdf
Data publikacji:
2014
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
extreme learning machine
palmprint recognition
superior speed
support vector machine (SVM)
Opis:
Support vector machine and artificial neural network are widely used in classification applications. Extreme learning machine (ELM) is a novel and efficient learning algorithm based on the generalized single hidden layer feed forward networks, which performs well in classification applications. The research results have shown the superiority of ELM with the existing classical algorithms: support vector machine (SVM) and back propagation neural network. In this study, we firstly propose a novel nonnegative matrix factorization extreme learning machine (NMFELM) to improve the performance of standard ELM method. Then we propose a novel near-infrared palmprint recognition approach based on NMFELM classifier. As the test data, we use the near-infrared palmprint database provided by Hong Kong Polytechnic University. The experimental results demonstrate that the proposed NMFELM method outperforms the standard ELM- and SVM-based methods.
Źródło:
Optica Applicata; 2014, 44, 2; 285-298
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Implementation of Bilinear Separation algorithm as a classification method for SSVEP-based brain-computer interface
Autorzy:
Jukiewicz, M.
Cysewska-Sobusiak, A.
Powiązania:
https://bibliotekanauki.pl/articles/114357.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
brain-computer interface
SSVEP
bilinear separation
support vector machine (SVM)
Opis:
: The aim of this study was to create a two-class brain-computer interface. As in the case of research on SSVEP stimuli flashing at different frequencies were presented to four subjects. Optimal SSVEP recognition results can be obtained from electrodes: O1, O2 and Oz. In this work SVM classifier with Bilinear Separation algorithm have been compared. The best result in the offline tests using Bilinear Separation was: average accuracy of stimuli recognition 93% and ITR 33.1 bit/min, SVM: 90% and 32.8 bit/min.
Źródło:
Measurement Automation Monitoring; 2015, 61, 2; 51-53
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method
Autorzy:
Cui, J.
Powiązania:
https://bibliotekanauki.pl/articles/220938.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
power electronic circuits
fault classification
support vector data description
support vector machine (SVM)
Opis:
Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.
Źródło:
Metrology and Measurement Systems; 2015, 22, 2; 205-220
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fall Detector Using Discrete Wavelet Decomposition And SVM Classifier
Autorzy:
Wójtowicz, B.
Dobrowolski, A.
Tomczykiewicz, K.
Powiązania:
https://bibliotekanauki.pl/articles/220495.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fall detection
discrete wavelet transform
data fusion
support vector machine (SVM)
Opis:
This paper presents the design process and the results of a novel fall detector designed and constructed at the Faculty of Electronics, Military University of Technology. High sensitivity and low false alarm rates were achieved by using four independent sensors of varying physical quantities and sophisticated methods of signal processing and data mining. The manuscript discusses the study background, hardware development, alternative algorithms used for the sensor data processing and fusion for identification of the most efficient solution and the final results from testing the Android application on smartphone. The test was performed in four 6-h sessions (two sessions with female participants at the age of 28 years, one session with male participants aged 28 years and one involving a man at the age of 49 years) and showed correct detection of all 40 simulated falls with only three false alarms. Our results confirmed the sensitivity of the proposed algorithm to be 100% with a nominal false alarm rate (one false alarm per 8 h).
Źródło:
Metrology and Measurement Systems; 2015, 22, 2; 303-314
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft Sensing Method Of LS-SVM Using Temperature Time Series For Gas Flow Measurements
Autorzy:
Xu, W.
Fan, Z.
Cai, M.
Shi, Y.
Tong, X.
Sun, J.
Powiązania:
https://bibliotekanauki.pl/articles/221824.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
gas flow
soft sensor
support vector machine (SVM)
temperature time series
Opis:
This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.
Źródło:
Metrology and Measurement Systems; 2015, 22, 3; 383-392
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Eclectic Approach to Network Service Failure Detection Based on Multicriteria Analysis with an Example of Mixing Probabilistic Context Free Grammar Models
Autorzy:
Białoń, P.
Powiązania:
https://bibliotekanauki.pl/articles/307950.pdf
Data publikacji:
2008
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
failure detection
linear separation
probabilistic context free grammars
support vector machine (SVM)
Opis:
A method of failure detection in telecommunication networks is presented. This is a meta-method that correlates alarms raised by failure-detection modules based on various philosophies. The correlation takes into account two main characteristics of each module and the whole metamethod: the percentage of false alarms and the percentage of omitted failures. The trade-off between them is tackled with aspiration-based multicriteria analysis. The alarms are correlated using linear classification by support vector machines. An example of the profitability of correlating alarms in such way is shown. This is an example of probabilistic context free grammars (PCFGs), used to model the proper runtime paths of network services (and thus usable for detecting an improper behavior of the services). It is shown that the linearly mixing PCFGs can add context handling to the PCFG model, thus augmenting the capabilities of the model.
Źródło:
Journal of Telecommunications and Information Technology; 2008, 4; 32-39
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A linear Support Vector Machine solver for a large number of training examples
Autorzy:
Białoń, P.
Powiązania:
https://bibliotekanauki.pl/articles/970794.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
support vector machine (SVM)
analytic center cutting plane method
RAM volume required
Opis:
A new linear Support Vector Machine algorithm and solver are presented. The algorithm is in a twofold way well-suited for problems with a large number of training examples. First, unlike many optimization algorithms, it does not simultaneously keep all the examples in RAM and thus does not exhaust the memory (moreover, it smartly passes through disk files storing the data: two mechanisms reduce the computation time by disregarding some input data without a loss in solution quality). Second, it uses the analytical center cutting plane scheme, appearing as more efficient for hard parameter settings than the Kelley's scheme used in other solvers, like SVM_perf. The experiments with both real-life and artificial examples are described. In one of them the solver proved to be capable of solving a problem with one billion training examples. A critical analysis of the complexity of SVM_perf is given.
Źródło:
Control and Cybernetics; 2009, 38, 1; 281-300
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
Autorzy:
Amer, F. Z.
El-Garhy, A. M.
Awadalla, M. H.
Rashad, S. M.
Abdien, A. K.
Powiązania:
https://bibliotekanauki.pl/articles/147652.pdf
Data publikacji:
2011
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
support vector machine (SVM)
fault classification
multi fault classification
genetic algorithm (GA)
machine learning
Opis:
Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.
Źródło:
Nukleonika; 2011, 56, 4; 323-332
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Study of Supervised Learning Methods for Malware Analysis
Autorzy:
Kruczkowski, M.
Niewiadomska-Szynkiewicz, E.
Powiązania:
https://bibliotekanauki.pl/articles/309481.pdf
Data publikacji:
2014
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
data classification
k-Nearest Neighbors
malware analysis
Naive Bayes
support vector machine (SVM)
Opis:
Malware is a software designed to disrupt or even damage computer system or do other unwanted actions. Nowadays, malware is a common threat of the World Wide Web. Anti-malware protection and intrusion detection can be significantly supported by a comprehensive and extensive analysis of data on the Web. The aim of such analysis is a classification of the collected data into two sets, i.e., normal and malicious data. In this paper the authors investigate the use of three supervised learning methods for data mining to support the malware detection. The results of applications of Support Vector Machine, Naive Bayes and k-Nearest Neighbors techniques to classification of the data taken from devices located in many units, organizations and monitoring systems serviced by CERT Poland are described. The performance of all methods is compared and discussed. The results of performed experiments show that the supervised learning algorithms method can be successfully used to computer data analysis, and can support computer emergency response teams in threats detection.
Źródło:
Journal of Telecommunications and Information Technology; 2014, 4; 24-33
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Learning System by the Least Squares Support Vector Machine Method and its Application in Medicine
Autorzy:
Szewczyk, P.
Baszun, M.
Powiązania:
https://bibliotekanauki.pl/articles/307897.pdf
Data publikacji:
2011
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
classification
Grid-Search
particle swarm optimization (PSO)
patients diagnosis
support vector machine (SVM)
Opis:
In the paper it has been presented the possibility of using the least squares support vector machine to the initial diagnosis of patients. In order to find some optimal parameters making the work of the algorithm more detailed, the following techniques have been used: K-fold Cross Validation, Grid-Search, Particle Swarm Optimization. The result of the classification has been checked by some labels assigned by an expert. The created system has been tested on the artificially made data and the data taken from the real database. The results of the computer simulations have been presented in two forms: numerical and graphic. All the algorithms have been implemented in the C# language.
Źródło:
Journal of Telecommunications and Information Technology; 2011, 3; 109-113
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq
Autorzy:
Alwan, Imzahim A.
Aziz, Nadia A.
Powiązania:
https://bibliotekanauki.pl/articles/1838006.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
land cover mapping
Sentinel 2
supervised classification
maximum likelihood
Support Vector Machine (SVM)
confusion matrix
Opis:
Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al Hawizeh marsh/Iraq Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.
Źródło:
Geomatics and Environmental Engineering; 2021, 15, 1; 5-21
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multisensoryczny detektor upadków wykorzystujący dyskretną dekompozycję falkową oraz klasyfikator SVM
A multisensor fall detector using the discrete wavelet decomposition and SVM classifier
Autorzy:
Wójtowicz, B.
Dobrowolski, A. P.
Powiązania:
https://bibliotekanauki.pl/articles/154406.pdf
Data publikacji:
2014
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
detekcja upadków
fuzja sensoryczna
sieć wektorów podtrzymujących
fall detection
data fusion
support vector machine (SVM)
Opis:
W artykule zaprezentowano wyniki badań opracowanego mechanizmu detekcji upadków. Wysoką niezawodność oraz niski poziom fałszywych alarmów uzyskano w wyniku zastosowania czterech niezależnych sensorów różnych wielkości fizycznych oraz wyrafinowanych metod przetwarzania sygnałów i eksploracji danych. Przeprowadzone badania pozwalają na stwierdzenie, że pominięcie znaku deskryptorów znacznie poprawia skuteczność prawidłowej klasyfikacji upadków. Z tego powodu w dalszych pracach zostanie przyjęty algorytm wykorzystujący wartości bezwzględne wyznaczanych cech. W trakcie badań zaobserwowano, że zwiększanie liczby cech użytych w procesie uczenia oraz testowania nie prowadzi do zwiększenia jakości klasyfikacji. Wynika stąd potrzeba dobrania optymalnej liczby deskryptorów. Dlatego istotnym warunkiem poprawy skuteczności systemu jest przeprowadzenie właściwej selekcji cech, co jest głównym celem kolejnego etapu badań.
The paper presents the results of research on a fall detection algorithm. The high reliability and a low level of false alarms were obtained by the use of four independent sensors of various physical quantities as well as sophisticated methods of signal processing and data mining. The algorithm was implemented and tested in Matlab. It was based on the discrete wavelet transform and a support vectors machine. The source of the data was processed by the detector presented in [5, 6]. The device integrates four MEMS sensors. It includes an atmospheric pressure sensor and three triaxial sensors, such as an accelerometer, a gyroscope and a magnetometer. The signal from each of the available sensors was sampled at a frequency of 25 Hz. The processed and analyzed frame had the length of 100 samples, which equaled four-second registration. The scheme of the measurement system is shown in Figure 3. The obtained findings were the basis for the presentation of each sensor in the field of ROC curves in two variants (taking into account an extracted feature with the sign and with its omission). Definitely, better results were obtained using the absolute values of the descriptors in the process of learning/testing. The best results of fall detection were received for a gyroscope and an accelerometer, followed by a magnetometer and a barometric pressure sensor. From the studies one can draw a conclusion that the omission of the sign descriptors significantly improves the correct classification of falls. For this reason, in further work there will be adopted an algorithm using the absolute values of extracted features. During the study it was observed that the increase in the number of features used in learning and testing did not lead to the increase in the quality of classification. This calls for the selection of the optimum number of descriptors. Therefore, an important prerequisite to improve the efficiency of the system is a proper feature selection, which is the main objective of the next stage of investigations. In further research, we plan to implement the data fusion algorithm in order to increase the effectiveness of the mechanisms developed.
Źródło:
Pomiary Automatyka Kontrola; 2014, R. 60, nr 9, 9; 729-732
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An analog linear SVM image classifier
Autorzy:
Festila, L.
Szolga, L. A.
Groza, R.
Hintea, S.
Cirlugea, M.
Powiązania:
https://bibliotekanauki.pl/articles/385015.pdf
Data publikacji:
2009
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
support vector machine (SVM)
analog multipliers
log-domain th domain
square-root domain
image classifier
Opis:
A linear Support Vector machine classifier is proposed in this paper. In such SVM architectures based on multiplying laws the main building blocks are multipliers. We propose in this paper multiplying and weighting cells, developed by using a model consisting of a compound of two inverse non-linear functions. This procedure is suitable for VLSI implementation because it permits the use of simple nonlinearized standard log-domain or DA cells that compensate each other nonlinearities to obtain an extended domain of operation. Current-mode ELIN (externally linear internally nonlinear) design is used for its low voltage, low power and high speed characteristics. The resulted parallel-serial classifier was simulated taking into account real parameters of transistors in BICMOS technology.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 2; 82-87
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multiple-instance learning with pairwise instance similarity
Autorzy:
Yuan, L.
Liu, J.
Tang, X.
Powiązania:
https://bibliotekanauki.pl/articles/330821.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
multiple instance learning
instance selection
similarity
support vector machine (SVM)
uczenie maszynowe
podobieństwo
metoda wektorów wspomagających
Opis:
Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency of the whole learning process. In this paper, we propose a simple and efficient ISMIL algorithm based on the similarity of pairwise instances within a bag. The basic idea is selecting from every training bag a pair of the most similar instances as instance prototypes and then mapping training bags into the embedding space that is constructed from all the instance prototypes. Thus, the MIL problem can be solved with the standard supervised learning techniques, such as support vector machines. Experiments show that the proposed algorithm is more efficient than its competitors and highly comparable with them in terms of classification accuracy. Moreover, the testing of noise sensitivity demonstrates that our MIL algorithm is very robust to labeling noise.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 567-577
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efficiency of linear and non-linear classifiers for gas identification from electrocatalytic gas sensor
Autorzy:
Kalinowski, P.
Woźniak, Ł.
Strzelczyk, A.
Jasinski, P.
Jasinski, G.
Powiązania:
https://bibliotekanauki.pl/articles/221796.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electrocatalytic sensor
cyclic voltammetry
data pre-processing
support vector machine (SVM)
Partial Least Squares Discriminant Analysis
Opis:
Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling.
Źródło:
Metrology and Measurement Systems; 2013, 20, 3; 501-512
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification
Autorzy:
Chmielnicki, W.
Stąpor, K.
Powiązania:
https://bibliotekanauki.pl/articles/330749.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
multiclass classification
pairwise coupling
problem decomposition
support vector machine (SVM)
klasyfikacja wieloklasowa
rozkład problemu
maszyna wektorów wspierających
Opis:
The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principle is to separate each pair of classes ignoring the remaining ones. Then all objects are tested against these classifiers and a voting scheme is applied using pairwise class probability estimates in a joint probability estimate for all classes. A closer look at the pairwise strategy shows the problem which impacts the final result. Each binary classifier votes for each object even if it does not belong to one of the two classes which it is trained on. This problem is addressed in our strategy. We propose to use additional classifiers to select the objects which will be considered by the pairwise classifiers. A similar solution was proposed by Moreira and Mayoraz (1998), but they use classifiers which are biased according to imbalance in the number of samples representing classes.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 191-201
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A primal sub-gradient method for structured classification with the averaged sum loss
Autorzy:
Mančev, D.
Todorović, B.
Powiązania:
https://bibliotekanauki.pl/articles/331050.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
structured classification
support vector machine (SVM)
subgradient method
sequence labeling
klasyfikacja strukturalna
maszyna wektorów nośnych
rozpoznawanie wzorca
Opis:
We present a primal sub-gradient method for structured SVM optimization defined with the averaged sum of hinge losses inside each example. Compared with the mini-batch version of the Pegasos algorithm for the structured case, which deals with a single structure from each of multiple examples, our algorithm considers multiple structures from a single example in one update. This approach should increase the amount of information learned from the example. We show that the proposed version with the averaged sum loss has at least the same guarantees in terms of the prediction loss as the stochastic version. Experiments are conducted on two sequence labeling problems, shallow parsing and part-of-speech tagging, and also include a comparison with other popular sequential structured learning algorithms.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 917-930
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computational intelligence methods in the problem of modelling technical wear of buildings in mining areas
Metody inteligencji obliczeniowej w problemie modelowania stopnia zużycia technicznego budynków na terenach górniczych
Autorzy:
Rusek, J.
Powiązania:
https://bibliotekanauki.pl/articles/385956.pdf
Data publikacji:
2012
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
technical wear
neural networks
support vector machine (SVM)
fuzzy systems
szkody górnicze
zużycie techniczne
sieci neuronowe
systemy rozmyte
Opis:
In the work presented approach with a view to building the model of degree of technical wear of buildings in the mining areas, as well as an indication that the contribution of the consumption on technical factors interact mining and civil construction origin. Set out criteria for the selection and research methodology effects are synthetically summarised existing work in this field. Justified choice of the ϵ-SVR method confronting its advantages to the characteristics of typical neural network.
W artykule zaprezentowano podejście mające na celu budowę modelu przebiegu stopnia zużycia technicznego budynków na terenach górniczych, jak również analizowano, w jakim stopniu na zużycie techniczne oddziałują czynniki górnicze oraz ogólnobudowlane. Przedstawiono kryteria doboru metodyki badań oraz podsumowano efekty dotychczasowych prac w tej dziedzinie. Uzasadniono wybór metody &vepsilon;-SVR, konfrontując jej zalety z własnościami typowych, jednokierunkowych sieci neuronowych. Opisano sposób optymalnego doboru parametrów charakteryzujących złożoność modelu ϵ-SVR oraz wskazano możliwość zastosowania tak utworzonego modelu w systemach ekspertowych.
Źródło:
Geomatics and Environmental Engineering; 2012, 6, 3; 83-91
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Comparative Study between CS-LBP/SVM and CS-LBP/PCA in Facial Expression Recognition
Autorzy:
Gaur, Sheena
Powiązania:
https://bibliotekanauki.pl/articles/1075570.pdf
Data publikacji:
2019
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Center symmetric local binary patterns (CS-LBP)
Facial Expression Analysis
Principal Component Analysis 9PCA)
Support Vector Machine (SVM)
Opis:
Face plays significant role in social communication. This is a 'window' to human personality, emotions and thoughts. Due to this, face is a subject of study in many areas of science such as psychology, behavioral science, medicine and computer science etc. In this paper, a comparative study is suggested between CS-LBP/SVM and CS-LBP/PCA. These algorithms are used in emotive facial expression recognition. Finally, a comparison is shown between PCA & SVM in terms of Dimension Reduction. The proposed system uses grayscale frontal face images of a Japanese female to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.
Źródło:
World Scientific News; 2019, 121; 83-89
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie maszyny wektorów nośnych oraz liniowej analizy dyskryminacyjnej jako klasyfikatorów cech w interfejsach mózg-komputer
Using support vector machine and linear discriminant analysis for features classification in brain-computer interfaces
Autorzy:
Jukiewicz, M.
Powiązania:
https://bibliotekanauki.pl/articles/376916.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
interfejs mózg-komputer
Maszyna Wektorów Nośnych
Liniowa Analiza Dyskryminacyjna
brain-computer interface
support vector machine (SVM)
linear discriminant analysis
Opis:
Głównym celem artykułu jest porównanie skuteczności klasyfikacji cech dwóch algorytmów klasyfikujących wykorzystywanych w interfejsach mózg-komputer: SVM (ang. Support Vector Machine, Maszyna Wektorów Nośnych) oraz LDA (ang. Linear Discriminant Analysis, Liniowa Analiza Dyskryminacyjna). W artykule przedstawiono interfejs, w którym użytkownikowi prezentowane są dwa bodźce migające z różną częstotliwością (10 i 15 Hz), a następnie za pomocą elektrod elektroencefalografu mierzona jest odpowiedź elektryczna mózgu. W takich interfejsach sygnał zbierany jest zwykle w okolicach potylicznych (nad korą wzrokową). W prezentowanym rozwiązaniu sygnał mierzony jest z okolic czołowych. W przetwarzaniu i analizie sygnału zastosowano algorytmy statystycznego uczenia maszynowego. Do ekstrakcji cech sygnału wykorzystano Szybką Transformatę Fouriera, do selekcji cech: test t-Welcha, a do klasyfikacji cech: SVM oraz DLA. Na podstawie odpowiedzi uzyskanej z klasyfikatora możliwe jest np. wysterowanie kierunku skrętu robota mobilnego lub włączenie czy wyłączenie oświetlenia.
The main aim of this article is to compare the effectiveness of the classification of the two classifiers used in brain-computer interfaces: SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis). The article presents an interface in which the subject is presented the two stimuli flashing at different frequencies (10 and 15 Hz) and then by using EEG electrodes electrical response of the brain is measured. In these interfaces, the signal is typically collected in the occipital area (on the visual cortex). In the presented solution the signal is measured form the prefrontal cortex. For signal processing and analysis statistical machine learning algorithms were used. For features’ extraction Fast Fourier Transform was used. For features’ selection Welch’s t test was used. For features’ classification was used SVM and DLA. Based on the responses obtained from the classifier it is possible to control the direction of a mobile robot’s movement or turning the lights on and off.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2014, 79; 25-30
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A support vector machine with the tabu search algorithm for freeway incident detection
Autorzy:
Yao, B.
Hu, P.
Zhang, M.
Jin, M.
Powiązania:
https://bibliotekanauki.pl/articles/329943.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automated incident detection
support vector machine (SVM)
tabu search
freeway
maszyna wektorów wspierających
odcinek swobodny trasy
algorytm tabu search
Opis:
Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 397-404
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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