An improved entropy-based multiple kernel learning

Hideitsu Hino, Tetsuji Ogawa

研究成果: Conference contribution

抜粋

Kernel methods have been successfully used in many practical machine learning problems. However, the problem of choosing a suitable kernel is left to practitioners. One method to select the optimal kernel is to learn a linear combination of element kernels. A framework of multiple kernel learning based on conditional entropy minimization criterion (MCEM) has been proposed and it has been shown to work well for, e.g., speaker recognition tasks. In this paper, a computationally efficient implementation for MCEM, which utilizes sequential quadratic programming, is formulated. Through a comparative experiment to conventional MCEM algorithm on a speaker verification task, the proposed method is shown to offer comparable verification accuracy with considerable improvement in computational speed.

元の言語English
ホスト出版物のタイトルICPR 2012 - 21st International Conference on Pattern Recognition
ページ1189-1192
ページ数4
出版物ステータスPublished - 2012 12 1
イベント21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
継続期間: 2012 11 112012 11 15

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
ISSN(印刷物)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
Japan
Tsukuba
期間12/11/1112/11/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

フィンガープリント An improved entropy-based multiple kernel learning' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Hino, H., & Ogawa, T. (2012). An improved entropy-based multiple kernel learning. : ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 1189-1192). [6460350] (Proceedings - International Conference on Pattern Recognition).