Optymalizacja statystycznych systemów uczących się

Jankowski, S.

Powiadomienia systemowe

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Artykuł - szczegóły

Tytuł artykułu

Optymalizacja statystycznych systemów uczących się

Autorzy

Jankowski S.

Identyfikatory

Warianty tytułu

Optimization of statistical learning systems

Języki publikacji

Abstrakty

Dysertacja prezentuje wyniki badań w dziedzinie statystycznych systemów uczących się. W części l zostały opisane nowe, oryginalne idee rozszerzające zdolności przetwarzania informacji za pomocą wielkich nieliniowych układów dynamicznych. Opracowano uogólnienie pamięci asocjacyjnej Hopfielda w dziedzinie liczb zespolonych - system ma zdolność odtwarzania wzorców o stanach wielowartościowych reprezentujących odcienie szarości lub kolory dzięki zastosowaniu funkcji signum zespolone jako funkcji przetwarzania neuronów do kodowania fazowego wektorów wielowartościowych. Opisano nowy model zespolonej pamięci asocjacyjnej, która jest w stanie rozpoznawać obrazy inwariantnie względem przesunięcia. Wykazano, że pamięci asocjacyjne zbudowane z układów dynamicznych wyższego rzędu: oscylatorów drgań okresowych i oscylatorów drgań chaotycznych są zdolne do segmentacji (dekorelacji) wzorców złożonych. Przedstawiono implementacje programowalnych komórkowych sieci neuronowych do ultraszybkiego przetwarzania obrazów w postaci scalonego układu elektronicznego oraz implementację optoelektroniczną z wykorzystaniem tyrystorów optycznych. Część 2 rozprawy jest poświęcona prezentacji statystycznej teorii uczenia wybranych systemów statycznych przeznaczonych do uczenia nadzorowanego w zadaniach klasyfikacji i regresji: perceptron wielowarstwowy, maszyna wektorów nośnych, średniokwadratowa maszyna wektorów nośnych, maszyna wektorów istotnych. Opisano metody optymalizacji systemów na podstawie statystyki wpływu - wirtualnej skrajnej oceny krzyżowej. Przedstawiono wyniki zastosowania statystycznych systemów uczących się do badania struktury defektowej materiałów wysokorezystywnych i półizolujących. Zrealizowano koncepcję klasyfikacji transdukcyjnej - uczenia częściowo nadzorowanego do wspomagania diagnozy kardiologicznej.

The dissertation presents the summary of research in the area of statistical learning systems. Part l is devoted to new, original ideas that enable extension of the ability of information processing by using large nonlinear dynamical systems. Generalization of the Hopfield associative memory in the complex number domain is able to retrieve multivalued patterns representing the gray-scaled and/ or colored images due to the application of complex signum neuron activation function to phasecoding of multivalued vectors. A new model of complex associative memory for shift invariant pattern recognition is presented. It was shown that associative memories composed of periodic and chaotic oscillating neurons are able to perform segmentation (decorrelation) of the composition of patterns. The implementations of programmable cellular neural networks for high speed image processing were presented in two forms: as an integrated electronic circuit and as an optoelectronic system based on optical thyristors. Part 2 presents the studies of statistical learning theory of selected static systems designed for supervised learning of classification and regression tasks: multilayer perceptron, support vector machine, least-square support vector machine, relevance vector machine. The optimization of these systems based on influential statistics - the virtual leave-one-out - was described. The original results of applications of statistical learning systems for analysis of defect structures of high resistive and semi-insulating materials were described. The idea of transductive classification – semisupervised learning system to computer-aided cardiologic diagnosis - was performed.

Słowa kluczowe

statystyczne systemy uczące się sieci neuronowe komórkowe sieci neuronowe maszyna wektorów nośnych pamięć asocjacyjna regresja statystyka wpływu transdukcja

statistical learning systems neural networks cellular neural networks support vector machine associative memory classification regression influential statistics transduction

Wydawca

Oficyna Wydawnicza Politechniki Warszawskiej

Czasopismo

Prace Naukowe Politechniki Warszawskiej. Elektronika

Rocznik

2012

Tom

z. 183

Strony

3--107

Opis fizyczny

Bibliogr. 249 poz., rys., tab., wykr.

Twórcy

autor

Jankowski S.

Instytut Systemów Elektronicznych

Bibliografia

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109. Jankowski S., Gierasimowicz H.: Monte Carlo Renormalization Group Approach to Random Network Models of Dielectric Breakdown, Proceedings of the XIV National Conference Circuit Theory and Electronic Circuits, Waplewo 1991, 248-253
110. Jankowski S., Gierasimowicz H., Węgrzynowski T.: Pomiary i modelowanie dyspersji elektrycznej wilgotnych materiałów ziarnistych, XVI Międzyuczelniana Konferencja Metrologów 1983, Wydawnictwa Uczelniane Politechniki Lubelskiej, Seria C nr 11, 1983, 224-229
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112. Jankowski S., Kamiński P., Będkowski J., Danilewicz R, Szymański Z., Kozłowski R., Pawlowski M.: Application of computational intelligence to analysis of PITS spectral images for defect centers in semi-insulating materials, Journal of Materials Science. Materials In Electronics (December 2008) Vol. 19: S356-S361, Springer (DRIP), ISSN 0957-4522
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114. Jankowski S., Knioła M., Kozłowski R,: Application of Least-Square Support Vector Machine (LS-SVM) to determination of deep level defect centers parameters in semi insulating GaAs, in Photonics Applications in Astronomy, Communications, Industry and High-Energy Experiments 2006, ed. by R.S. Romaniuk, Proc. of SPIE vol. 6347 SPIE, Bellingham WA 2006, 6347-109
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Bibliografia

Identyfikator YADDA

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