An improved entropy-based multiple kernel learning

Hideitsu Hino*, Tetsuji Ogawa

*Corresponding author for this work

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


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.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Number of pages4
Publication statusPublished - 2012 Dec 1
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 2012 Nov 112012 Nov 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference21st International Conference on Pattern Recognition, ICPR 2012

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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