Approximate Learning Algorithm for Restricted Boltzmann Machines

被引：5
|
作者：
Yasuda, Muneki [1 ]
Tanaka, Kazuyuki [1 ]
机构：
[1] Tohoku Univ, Grad Sch Informat Sci, Aoba Ku, Sendai, Miyagi 9808579, Japan
来源：
2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING CONTROL & AUTOMATION, VOLS 1 AND 2 | 2008年
关键词：
D O I：
10.1109/CIMCA.2008.57
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A restricted Boltzmann machine consists of a layer of visible units and a layer of hidden units with no visible-visible or hidden-hidden connections. The restricted Boltzmann machine is the main component used in building up the deep belief network and has been studied by many researchers. However, the learning algorithm for the restricted Boltzmann machine is a NP-hard problem in general. In this paper we propose a new approximate learning algorithm for the restricted Boltzmann machines using the EM algorithm and the loopy belief propagation.
引用
收藏
页码：692 / 697
页数：6
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