Machine learning by a subset of hypotheses

Takafumi Mukouchi; Toshiyasu Matsushima; Shigeichi Hirasawa

Machine learning by a subset of hypotheses

Takafumi Mukouchi^*, Toshiyasu Matsushima, Shigeichi Hirasawa

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Pages (from-to)	2533-2538
Number of pages	6
Journal	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume	3
Publication status	Published - 1997 Dec 1
Externally published	Yes
Event	Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA Duration: 1997 Oct 12 → 1997 Oct 15

ASJC Scopus subject areas

Control and Systems Engineering
Hardware and Architecture

Cite this

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title = "Machine learning by a subset of hypotheses",

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.",

author = "Takafumi Mukouchi and Toshiyasu Matsushima and Shigeichi Hirasawa",

year = "1997",

month = dec,

day = "1",

language = "English",

volume = "3",

pages = "2533--2538",

journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",

issn = "0884-3627",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) ; Conference date: 12-10-1997 Through 15-10-1997",

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TY - JOUR

T1 - Machine learning by a subset of hypotheses

AU - Mukouchi, Takafumi

AU - Matsushima, Toshiyasu

AU - Hirasawa, Shigeichi

PY - 1997/12/1

Y1 - 1997/12/1

N2 - 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.

AB - 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.

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M3 - Conference article

AN - SCOPUS:0031375754

SN - 0884-3627

VL - 3

SP - 2533

EP - 2538

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

T2 - Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5)

Y2 - 12 October 1997 through 15 October 1997

ER -

Machine learning by a subset of hypotheses

Abstract

ASJC Scopus subject areas

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