期刊
NEURAL NETWORKS
卷 64, 期 -, 页码 19-28出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2014.09.007
关键词
Deep learning; Support vector machine; Class probability output network; Uncertainty measure
资金
- ICT R&D program of MSIP/IITP [10041826]
- Industrial Strategic Technology Development Program - Ministry of Knowledge Economy (MIKE, Korea) [10044009]
- Korea Evaluation Institute of Industrial Technology (KEIT) [10041826] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Ministry of Public Safety & Security (MPSS), Republic of Korea [R0101-15-0162] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. (C) 2014 Elsevier Ltd. All rights reserved.
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