Non-parametric e-mixture of density functions

Hideitsu Hino, Ken Takano, Shotaro Akaho, Noboru Murata

    研究成果: Conference contribution

    1 被引用数 (Scopus)

    抄録

    Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m-and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.

    本文言語English
    ホスト出版物のタイトルNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
    出版社Springer Verlag
    ページ3-10
    ページ数8
    9948 LNCS
    ISBN(印刷版)9783319466712
    DOI
    出版ステータスPublished - 2016
    イベント23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
    継続期間: 2016 10 162016 10 21

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    9948 LNCS
    ISSN(印刷版)03029743
    ISSN(電子版)16113349

    Other

    Other23rd International Conference on Neural Information Processing, ICONIP 2016
    CountryJapan
    CityKyoto
    Period16/10/1616/10/21

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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