文章基本信息

标题：Pseudo Temperature of Observed Data
本地全文：下载
作者：Muneki YASUDA
期刊名称：Interdisciplinary Information Sciences
印刷版ISSN：1340-9050
电子版ISSN：1347-6157
出版年度：2012
卷号：18
期号：2
页码：185-188
DOI：10.4036/iis.2012.185
出版社：The Editorial Committee of the Interdisciplinary Information Sciences
摘要：A Boltzmann machine (BM) is a basic learning model forming a Markov random field, and many approximate learning algorithms for it so far. In the present paper, a new strategy for approximate BM learnings is proposed by introducing a temperature of observed data which controls a smoothness of empirical distribution. By controlling the temperature, one can obtain better solutions to BM learning with an existing approximate learning algorithm.