Nonlinear Theory and Its Applications, IEICE
Online ISSN : 2185-4106
ISSN-L : 2185-4106
Special Section on Current Topics on Neural Networks and Stochastic Models for Information Processing
Learning algorithm in restricted Boltzmann machines using Kullback-Leibler importance estimation procedure
Muneki YasudaTetsuharu SakuraiKazuyuki Tanaka
Author information

2011 Volume 2 Issue 2 Pages 153-164


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.

Information related to the author
© 2011 The Institute of Electronics, Information and Communication Engineers
Previous article Next article