### 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 |
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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

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## Cite this

Mukouchi, T., Matsushima, T., & Hirasawa, S. (1997). Machine learning by a subset of hypotheses.

*Proceedings of the IEEE International Conference on Systems, Man and Cybernetics*,*3*, 2533-2538.