Machine learning by a subset of hypotheses

Takafumi Mukouchi, Toshiyasu Matsushima, Shigeichi Hirasawa

Research output: Contribution to journalConference article

Abstract

Bayesian theory is effective in statistics, lossless, source coding, machine learning, etc. It is often, however, computationally expensive since the calculation of posterior probabilities and of mixture distributions is not tractable. In this paper, we propose a new method for approximately calculating mixture distributions in a discrete hypothesis class.

Original languageEnglish
Pages (from-to)2533-2538
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume3
Publication statusPublished - 1997 Dec 1
Externally publishedYes
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA
Duration: 1997 Oct 121997 Oct 15

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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