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Stacked Autoencoder를 이용한 특징 추출 기반 Fuzzy k-Nearest Neighbors 패턴 분류기 설계
노석범(Suck-Bum Rho),오성권(Sung-Kwun Oh) 대한전기학회 2015 전기학회논문지 Vol.64 No.1
In this paper, we propose a feature extraction method using the stacked autoencoders which consist of restricted Boltzmann machines. The stacked autoencoders is a sort of deep networks. Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. In terms of pattern classification problem, the feature extraction is a key issue. We use the stacked autoencoders networks to extract new features which have a good influence on the improvement of the classification performance. After feature extraction, fuzzy k-nearest neighbors algorithm is used for a classifier which classifies the new extracted data set. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.