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2015 | 25 | 4 | 915-925
Tytuł artykułu

Statistical testing of segment homogeneity in classification of piecewise-regular objects

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We propose a mathematical model of composite object representation as a sequence of independent segments. Each segment is represented as a random sample of independent identically distributed feature vectors. Based on this model and a statistical approach, we reduce the task to a problem of composite hypothesis testing of segment homogeneity. Several nearest-neighbor criteria are implemented, and for some of them the well-known special cases (e.g., the Kullback-Leibler minimum information discrimination principle, the probabilistic neural network) are highlighted. It is experimentally shown that the proposed approach improves the accuracy when compared with contemporary classifiers.
Rocznik
Tom
25
Numer
4
Strony
915-925
Opis fizyczny
Daty
wydano
2015
otrzymano
2014-11-01
poprawiono
2015-03-25
Twórcy
  • Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, 136 Rodionova St., Nizhny Novgorod 603093, Russia
  • Faculty of Computer Science, National Research University Higher School of Economics, 20 Myasnitskaya St., Moscow 101000, Russia
Bibliografia
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  • Huang, J.-T., Li, J., Yu, D., Deng, L. and Gong, Y. (2013). Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, pp. 7304-7308.
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  • Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60(2): 91-110.
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  • Savchenko, A.V. (2013b). Probabilistic neural network with homogeneity testing in recognition of discrete patterns set, Neural Networks 46: 227-241.
  • Savchenko, A.V. and Khokhlova, Y.I. (2014). About neural-network algorithms application in viseme classification problem with face video in audiovisual speech recognition systems, Optical Memory and Neural Networks (Information Optics) 23(1): 34-42.
  • Specht, D.F. (1990). Probabilistic neural networks, Neural Networks 3(1): 109-118.
  • Świercz, E. (2010). Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution, International Journal of Applied Mathematics and Computer Science 20(1): 135-147, DOI: 10.2478/v10006-010-0010-x.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv25i4p915bwm
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