Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "dimension reduction" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-13 z 13
    Tytuł:
    Asymptotic results for sliced inverse regression
    Asymptotyczne rezultaty dla „sliced inverse regression”
    Autorzy:
    Kötter, Thomas
    Powiązania:
    https://bibliotekanauki.pl/articles/904627.pdf
    Data publikacji:
    1997
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    dimension reduction
    inverse regression
    linear projections
    Opis:
    It is well known that nonparametric regression techniques do not have good performance in high dimensional regression. However nonparametric regression is successful in one- or low-dimensional regression problems and is much more flexible than the parametric alternative. Hence, for high dimensional regression tasks one would like to reduce the regressor space to a lower dimension and then use nonparametric methods for curve estimation. A possible dimension reduction approach is Sliced Inverse Regression (L i 1991). It allows to find a base of a subspace in the regressor space which still carries important information for the regression. The vectors spanning this subspace are found with a technique similar to Principal Component Analysis and can be judged with the eigenvalues that belong to these vectors. Asymptotic and simulation results for the eigenvalues and vectors are presented.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 1997, 141
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Python Machine Learning. Dry Beans Classification Case
    Autorzy:
    Słowiński, Grzegorz
    Powiązania:
    https://bibliotekanauki.pl/articles/50091919.pdf
    Data publikacji:
    2024-09
    Wydawca:
    Warszawska Wyższa Szkoła Informatyki
    Tematy:
    machine learning
    deep learning
    data dimension reduction
    activation function
    Opis:
    A dataset containing over 13k samples of dry beans geometric features was analyzed using machine learning (ML) and deep learning (DL) techniques with the goal to automatically classify the bean species. Performance in terms of accuracy, train and test time was analyzed. First the original dataset was reduced to eliminate redundant features (too strongly correlated and echoing others). Then the dataset was visualized and analyzed with a few shallow learning techniques and simple artificial neural network. Cross validation was used to check the learning process repeatability. Influence of data preparation (dimension reduction) on shallow learning techniques were observed. In case of Multilayer Perceptron 3 activation functions were tried: ReLu, ELU and sigmoid. Random Forest appeared to be the best model for dry beans classification task reaching average accuracy reaching 92.61% with reasonable train and test times.
    Źródło:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2024, 18, 30; 7-26
    1896-396X
    2082-8349
    Pojawia się w:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new low SNR underwater acoustic signal classification method based on intrinsic modal features maintaining dimensionality reduction
    Autorzy:
    Ju, Yang
    Wei, Zhengxian
    Li, Huangfu
    Feng, Xiao
    Powiązania:
    https://bibliotekanauki.pl/articles/259300.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    acoustic
    low SNR
    signal classification
    feature maintain
    dimension reduction
    Opis:
    The classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic. . This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the proces of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.
    Źródło:
    Polish Maritime Research; 2020, 2; 187-198
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A comparative study for outlier detection methods in high dimensional text data
    Autorzy:
    Park, Cheong Hee
    Powiązania:
    https://bibliotekanauki.pl/articles/2201316.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    curse of dimensionality
    dimension reduction
    high dimensional text data
    outlier detection
    Opis:
    Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semisupervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 1; 5--17
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dimension reduction for objects composed of vector sets
    Autorzy:
    Szemenyei, M.
    Vajda, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/330024.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    dimension reduction
    discriminant analysis
    object recognition
    registration
    redukcja wymiaru
    analiza dyskryminacyjna
    rozpoznawanie obiektu
    Opis:
    Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2017, 27, 1; 169-180
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid approach to dimension reduction in classification
    Autorzy:
    Krawczak, M.
    Szkatuła, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/206425.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    data series
    dimension reduction
    envelopes
    essential attributes
    heteroassociation
    machine learning from examples
    decision rules
    classification
    Opis:
    In this paper we introduce a hybrid approach to data series classification. The approach is based on the concept of aggregated upper and lower envelopes, and the principal components here called 'essential attributes', generated by multilayer neural networks. The essential attributes are represented by outputs of hidden layer neurons. Next, the real valued essential attributes are nominalized and symbolic data series representation is obtained. The symbolic representation is used to generate decision rules in the IF. . . THEN. . . form for data series classification. The approach reduces the dimension of data series. The efficiency of the approach was verified by considering numerical examples.
    Źródło:
    Control and Cybernetics; 2011, 40, 2; 527-551
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An algorithm for reducing the dimension and size of a sample for data exploration procedures
    Autorzy:
    Kulczycki, P.
    Łukasik, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/330110.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    dimension reduction
    sample size reduction
    linear transformation
    simulated annealing
    data mining
    redukcja wymiaru
    transformacja liniowa
    wyżarzanie symulowane
    eksploracja danych
    Opis:
    The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease in importance is performed on those data set elements which have undergone a significant change in location in relation to the others. The presented method can have universal application in a wide range of data exploration problems, offering flexible customization, possibility of use in a dynamic data environment, and comparable or better performance with regards to the principal component analysis. Its positive features were verified in detail for the domain’s fundamental tasks of clustering, classification and detection of atypical elements (outliers).
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 133-149
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A wavelet-based approach for business protocol discovery of web services from log files
    Autorzy:
    Moudjari, A.
    Kezzouli, I.
    Talbi, H.
    Draa, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/201169.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    business protocol discovery
    wavelets
    log files
    denoising
    dimension reduction
    protokół biznesowy
    falki
    pliki dziennika
    redukcja wymiarów
    Opis:
    Recently, business protocol discovery has taken more attention in the field of web services. This activity permits a better description of the web service by giving information about its dynamics. The latter is not supported by theWSDL language which concerns only the static part. The problem is that the only information available to construct the dynamic part is the set of log files saving the runtime interaction of the web service with its clients. In this paper, a new approach based on the Discrete Wavelet Transformation (DWT) is proposed to discover the business protocol of web services. The DWT allows reducing the problem space while preserving essential information. It also overcomes the problem of noise in the log files. The proposed approach has been validated using artificially-generated log files.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 535-546
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of correlation based dimension reduction methods
    Autorzy:
    Shin, Y. J.
    Park, C. H.
    Powiązania:
    https://bibliotekanauki.pl/articles/907508.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    analiza korelacyjna
    redukcja wymiaru
    liniowa analiza dyskryminacji
    canonical correlation analysis
    dimension reduction
    discriminative canonical correlation analysis
    linear discriminant analysis
    Opis:
    Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method. It has been shown that the classification performance of DCCA is superior to that of CCA due to the discriminative power using class label information. On the other hand, Linear Discriminant Analysis (LDA) is a supervised dimension reduction method and it is known as a special case of CCA. In this paper, we analyze the relationship between DCCA and LDA, showing that the projective directions by DCCA are equal to the ones obtained from LDA with respect to an orthogonal transformation. Using the relation with LDA, we propose a new method that can enhance the performance of DCCA. The experimental results show that the proposed method exhibits better classification performance than the original DCCA.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 3; 549-558
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Random projection RBF nets for multidimensional density estimation
    Autorzy:
    Skubalska-Rafajłowicz, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/929907.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    radialne funkcje bazowe
    estymacja
    wielowymiarowa gęstość prawdopodobieństwa
    redukcja wymiaru
    rzutowanie losowe
    detekcja nowości
    radial basis functions
    multivariate density estimation
    dimension reduction
    normal random projection
    novelty detection
    Opis:
    The dimensionality and the amount of data that need to be processed when intensive data streams are observed grow rapidly together with the development of sensors arrays, CCD and CMOS cameras and other devices. The aim of this paper is to propose an approach to dimensionality reduction as a first stage of training RBF nets. As a vehicle for presenting the ideas, the problem of estimating multivariate probability densities is chosen. The linear projection method is briefly surveyed. Using random projections as the first (additional) layer, we are able to reduce the dimensionality of input data. Bounds on the accuracy of RBF nets equipped with a random projection layer in comparison to RBF nets without dimensionality reduction are established. Finally, the results of simulations concerning multidimensional density estimation are briefly reported.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2008, 18, 4; 455-464
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    One-Dimensional Kohonens Lvq Nets for Multidimensional Patterns Recognition
    Autorzy:
    Skubalska-Rafajłowicz, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/911149.pdf
    Data publikacji:
    2000
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    krzywa przestrzenna
    rozpoznawanie obrazów
    space-filling curve
    pattern recognition
    learning vector quantization
    reduction of dimension
    Opis:
    A new neural network based pattern recognition algorithm is proposed. The method consists in preprocessing the multidimensional data, using a space-filling curve based transformation into the unit interval, and employing Kohonen's vector quantization algorithms (of SOM and LVQ types) in one dimension. The space-filling based transformation preserves the theoretical Bayes risk. Experiments show that such an approach can produce good or even better error rates than the classical LVQ performed in a multidimensional space.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 767-778
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effective energy integral functionals for thin films with three dimensional bending moment in the Orlicz-Sobolev space setting
    Autorzy:
    Laskowski, Włodzimierz
    Nguyen, Hong
    Powiązania:
    https://bibliotekanauki.pl/articles/729638.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
    Tematy:
    Γ-convergence
    3D-2D dimension reduction
    quasiconvex relaxation
    minimizers of variational integral functionals
    thin films
    elastic membranes
    effective energy integral functional
    bulk and surface energy
    equilibrium states of the film
    non-power-growth-type bulk energy density
    reflexive Orlicz and Orlicz-Sobolev spaces
    Opis:
    In this paper we consider an elastic thin film ω ⊂ ℝ² with the bending moment depending also on the third thickness variable. The effective energy functional defined on the Orlicz-Sobolev space over ω is described by Γ-convergence and 3D-2D dimension reduction techniques. Then we prove the existence of minimizers of the film energy functional. These results are proved in the case when the energy density function has the growth prescribed by an Orlicz convex function M. Here M is assumed to be non-power-growth-type and to satisfy the conditions Δ₂ and ∇₂.
    Źródło:
    Discussiones Mathematicae, Differential Inclusions, Control and Optimization; 2016, 36, 1; 7-31
    1509-9407
    Pojawia się w:
    Discussiones Mathematicae, Differential Inclusions, Control and Optimization
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analiza wpływu zastosowania redukcji poziomu szumu losowego na poziom ryzyka inwestycyjnego
    An analysis of the effect of noise reduction on the level of investment risk
    Autorzy:
    Zeug-Żebro, Katarzyna
    Powiązania:
    https://bibliotekanauki.pl/articles/585760.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Analiza R/S
    Metoda segmentowo-wariacyjna
    Redukcja szumu losowego
    Ryzyko inwestycyjne
    Wymiar fraktalny
    Fractal dimension
    Investment risk
    Noise reduction
    R/S analysis
    Segment-variation method
    Opis:
    W pracy przeprowadzono analizę wpływu zastosowania redukcji szumu losowego na poziom ryzyka inwestycyjnego mierzonego nieklasyczną miarą ryzyka, tj. wymiarem fraktalnym. Wymiar fraktalny jest jedną z charakterystyk dynamiki chaotycznej i bada, w jakim stopniu analizowany obiekt (szereg) wypełnia przestrzeń, w której jest zanurzony. W inwestycjach miara ta określa zmienność stopy zwrotu i im większa jej wartość, tym większe ryzyko związane z inwestowaniem w dany instrument finansowy. W celu wyznaczenia wymiaru fraktalnego zastosowano metodę segmentowo-wariacyjną i analizę R/S. W badaniach pod uwagę wzięto finansowe szeregi czasowe złożone z cen zamknięcia wybranych indeksów giełdowych oraz akcji spółek notowanych na GPW w Warszawie.
    The paper analyzes the impact of the use of noise reduction on the level of investment risk, measured by a non-classical risk measure, i.e. the fractal dimension. The fractal dimension is one of the characteristics of chaotic dynamics and study the extent to which an object (series) fills the space in which it is embedded. In investments, this measure determines the volatility of the rate of return and the greater its value, the greater the risk associated with investing in a given financial instrument. The segment- -variation method and R/S analysis were used to determine the fractal dimension. The test will be conducted based on the financial time series which consist of closing prices of stock market indices and companies listed on the Warsaw Stock Exchange.
    Źródło:
    Studia Ekonomiczne; 2017, 335; 77-90
    2083-8611
    Pojawia się w:
    Studia Ekonomiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-13 z 13

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies