Multiple kernel learning by conditional entropy minimization

Hideitsu Hino; Nima Reyhani; Noboru Murata

doi:10.1109/ICMLA.2010.40

Multiple kernel learning by conditional entropy minimization

Hideitsu Hino^*, Nima Reyhani, Noboru Murata

^*Corresponding author for this work

School of Advanced Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Kernel methods have been successfully used in many practical machine learning problems. Choosing a suitable kernel is left to the practitioner. A common way to an automatic selection of optimal kernels is to learn a linear combination of element kernels. In this paper, a novel framework of multiple kernel learning is proposed based on conditional entropy minimization criterion. For the proposed framework, three multiple kernel learning algorithms are derived. The algorithms are experimentally shown to be comparable to or outperform kernel Fisher discriminant analysis and other multiple kernel learning algorithms on benchmark data sets.

Original language	English
Title of host publication	Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Pages	223-228
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLA.2010.40
Publication status	Published - 2010 Dec 1
Event	9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States Duration: 2010 Dec 12 → 2010 Dec 14

Publication series

Name	Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Conference

Conference	9th International Conference on Machine Learning and Applications, ICMLA 2010
Country/Territory	United States
City	Washington, DC
Period	10/12/12 → 10/12/14

Keywords

Discriminant analysis
Entropy
Kernel methods
Multiple Kernel Learning

ASJC Scopus subject areas

Computer Science Applications
Human-Computer Interaction

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10.1109/ICMLA.2010.40

Cite this

Multiple kernel learning by conditional entropy minimization. / Hino, Hideitsu; Reyhani, Nima; Murata, Noboru.

Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. 2010. p. 223-228 5708837 (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hino, H, Reyhani, N & Murata, N 2010, Multiple kernel learning by conditional entropy minimization. in Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010., 5708837, Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010, pp. 223-228, 9th International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, United States, 10/12/12. https://doi.org/10.1109/ICMLA.2010.40

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AU - Murata, Noboru

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N2 - Kernel methods have been successfully used in many practical machine learning problems. Choosing a suitable kernel is left to the practitioner. A common way to an automatic selection of optimal kernels is to learn a linear combination of element kernels. In this paper, a novel framework of multiple kernel learning is proposed based on conditional entropy minimization criterion. For the proposed framework, three multiple kernel learning algorithms are derived. The algorithms are experimentally shown to be comparable to or outperform kernel Fisher discriminant analysis and other multiple kernel learning algorithms on benchmark data sets.

AB - Kernel methods have been successfully used in many practical machine learning problems. Choosing a suitable kernel is left to the practitioner. A common way to an automatic selection of optimal kernels is to learn a linear combination of element kernels. In this paper, a novel framework of multiple kernel learning is proposed based on conditional entropy minimization criterion. For the proposed framework, three multiple kernel learning algorithms are derived. The algorithms are experimentally shown to be comparable to or outperform kernel Fisher discriminant analysis and other multiple kernel learning algorithms on benchmark data sets.

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KW - Entropy

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Multiple kernel learning by conditional entropy minimization

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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