α-EM learning and its cookbook: From mixture-of-expert neural networks to movie random field

Yasuo Matsuyama, Takayuki Ikeda, Tomoaki Tanaka, Satoshi Furukawa, Naoki Takeda, Takeshi Niimoto

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

    2 Citations (Scopus)

    Abstract

    The α-EM algorithm is a proper extension of the traditional log-EM algorithm. This new algorithm is based on the α-logarithm, while the traditional one uses the logarithm. The case of α = -1 corresponds to the log-EM algorithm. Since the speed of the α-EM algorithm was reported for learning problems, this paper shows that closed-form E-steps can be obtained for a wide class of problems. There is a set of common techniques. That is, a cookbook for the α-EM algorithm is presented. The recipes include unsupervised neural networks, supervised neural networks for various gating, hidden Markov models and Markov random fields for moving object segmentation. Reasoning for the speedup is also given.

    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Place of PublicationUnited States
    PublisherIEEE
    Pages1368-1373
    Number of pages6
    Volume2
    Publication statusPublished - 1999
    EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
    Duration: 1999 Jul 101999 Jul 16

    Other

    OtherInternational Joint Conference on Neural Networks (IJCNN'99)
    CityWashington, DC, USA
    Period99/7/1099/7/16

    ASJC Scopus subject areas

    • Software

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