Learning algorithm in restricted Boltzmann machines using Kullback-Leibler importance estimation procedure
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概要
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Restricted Boltzmann machines (RBMs) are bipartite structured statistical neural networks and consist of two layers. One of them is a layer of visible units and the other one is a layer of hidden units. In each layer, any units do not connect to each other. RBMs have high flexibility and rich structure and have been expected to applied to various applications, for example, image and pattern recognitions, face detections and so on. However, most of computational models in RBMs are intractable and often belong to the class of NP-hard problem. In this paper, in order to construct a practical learning algorithm for them, we employ the Kullback-Leibler Importance Estimation Procedure (KLIEP) to RBMs, and give a new scheme of practical approximate learning algorithm for RBMs based on the KLIEP.
著者
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Yasuda Muneki
Graduate School of Information Science, Tohoku University, Sendai 980-8579, Japan
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YASUDA Muneki
Graduate School of Information Sciences, Tohoku University
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Tanaka Kazuyuki
Graduate School of Information Science, Tohoku University, Sendai 980-8579, Japan
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Sakurai Tetsuharu
Graduate School of Information Sciences, Tohoku University
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