Fast α-weighted EM learning for neural networks of module mixtures

Yasuo Matsuyama, Satoshi Furukawa, Naoki Takeda, Takayuki Ikeda

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Citations (Scopus)

    Abstract

    A class of extended logarithms is used to derive α-weighted EM (α-weighted Expectation and Maximization) algorithms. These extended EM algorithms (WEM's, α-EM's) have been anticipated to outperform the traditional (logarithmic) EM algorithm on the speed. The traditional approach falls into a special case of the new WEM. In this paper, general theoretical discussions are given first. Then, clear-cut evidences that show faster convergence than the ordinary EM approach are given on the case of mixture-of-expert neural networks. This process takes three steps. The first step is to show concrete algorithms. Then, the convergence is theoretically checked. Thirdly, experiments on the mixture-of-expert learning are tried to show the superiority of the WEM. Besides the supervised learning, unsupervised case on a Gaussian mixture is also examined. Faster convergence of the WEM is observed again.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
    Editors Anon
    Place of PublicationPiscataway, NJ, United States
    PublisherIEEE
    Pages2306-2311
    Number of pages6
    Volume3
    Publication statusPublished - 1998
    EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
    Duration: 1998 May 41998 May 9

    Other

    OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
    CityAnchorage, AK, USA
    Period98/5/498/5/9

    ASJC Scopus subject areas

    • Software

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