Presentation 2014-11-17
Feature Extraction for Image Classification using Restricted Boltzmann Machines
Reiki SUDA, Koujin TAKEDA,
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Abstract(in English) Learning restricted Boltzmann machines (RBMs) for high-dimensional data using maximum likelihood estimation had been faced with computational complexity. However, after the development of approximation algorithms based on Markov chain Monte Carlo methods, RBMs have been applicable even to high-dimensional data, and playing a central role as feature extractors. In this study, we conduct experiments of feature extraction from handwritten digit images using RBMs and of classification with the extracted features. As classifiers, fast and simple linear classifiers based on confidence-weighted learning are used. Finally, we show that classifiers trained with features extracted by RBMs outperform ones trained with raw data, and from the result we also evaluate the learning performance of the RBMs.
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Keyword(in English) restricted Boltzmann machines / image recognition / feature extraction / Markov chain Monte Carlo methods / linear classifiers / confidence-weighted learning
Paper # IBISML2014-36
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Committee IBISML
Conference Date 2014/11/10(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Feature Extraction for Image Classification using Restricted Boltzmann Machines
Sub Title (in English)
Keyword(1) restricted Boltzmann machines
Keyword(2) image recognition
Keyword(3) feature extraction
Keyword(4) Markov chain Monte Carlo methods
Keyword(5) linear classifiers
Keyword(6) confidence-weighted learning
1st Author's Name Reiki SUDA
1st Author's Affiliation Department of Intelligent Systems Engineering, Ibaraki University()
2nd Author's Name Koujin TAKEDA
2nd Author's Affiliation Department of Intelligent Systems Engineering, Ibaraki University
Date 2014-11-17
Paper # IBISML2014-36
Volume (vol) vol.114
Number (no) 306
Page pp.pp.-
#Pages 7
Date of Issue