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Restricted Boltzmann Machine with Multivalued Hidden Variables

Yuuki Yokoyama, Tomu Katsumata and Muneki Yasuda ()
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Yuuki Yokoyama: ALBERT Inc.
Tomu Katsumata: Yamagata University
Muneki Yasuda: Yamagata University

The Review of Socionetwork Strategies, 2019, vol. 13, issue 2, 253-266

Abstract: Abstract Generalization is one of the most important issues in machine learning problems. In this study, we consider generalization in restricted Boltzmann machines (RBMs). We propose an RBM with multivalued hidden variables, which is a simple extension of conventional RBMs. We demonstrate that the proposed model is better than the conventional model via numerical experiments for contrastive divergence learning with artificial data and a classification problem with MNIST.

Keywords: Statistical machine learning; Restricted Boltzmann machine; Pattern recognition; Generalization (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:spr:trosos:v:13:y:2019:i:2:d:10.1007_s12626-019-00042-4