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

^*Tämän työn vastaava kirjoittaja

Waseda University

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

7 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Sivut	223-228
Sivumäärä	6
DOI - pysyväislinkit	https://doi.org/10.1109/ICMLA.2010.40
Tila	Julkaistu - 1 jouluk. 2010
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Machine Learning and Applications - Washington, Yhdysvallat Kesto: 12 jouluk. 2010 → 14 jouluk. 2010 Konferenssinumero: 9

Conference

Conference	IEEE International Conference on Machine Learning and Applications
Lyhennettä	ICMLA
Maa/Alue	Yhdysvallat
Kaupunki	Washington
Ajanjakso	12/12/2010 → 14/12/2010

Pääsy asiakirjaan

10.1109/ICMLA.2010.40

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@inproceedings{006578ef485143b6b9bbdb17cc8168ba,

title = "Multiple kernel learning by conditional entropy minimization",

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.",

keywords = "Discriminant analysis, Entropy, Kernel methods, Multiple Kernel Learning",

author = "Hideitsu Hino and Nima Reyhani and Noboru Murata",

year = "2010",

month = dec,

day = "1",

doi = "10.1109/ICMLA.2010.40",

language = "English",

isbn = "9780769543000",

pages = "223--228",

booktitle = "Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010",

note = "IEEE International Conference on Machine Learning and Applications, ICMLA ; Conference date: 12-12-2010 Through 14-12-2010",

}

Hino, H, Reyhani, N & Murata, N 2010, Multiple kernel learning by conditional entropy minimization. julkaisussa Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010., 5708837, Sivut 223-228, IEEE International Conference on Machine Learning and Applications, Washington, District of Columbia, Yhdysvallat, 12/12/2010. https://doi.org/10.1109/ICMLA.2010.40

TY - GEN

T1 - Multiple kernel learning by conditional entropy minimization

AU - Hino, Hideitsu

AU - Reyhani, Nima

AU - Murata, Noboru

N1 - Conference code: 9

PY - 2010/12/1

Y1 - 2010/12/1

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.

KW - Discriminant analysis

KW - Entropy

KW - Kernel methods

KW - Multiple Kernel Learning

UR - https://www.scopus.com/pages/publications/79952420541

U2 - 10.1109/ICMLA.2010.40

DO - 10.1109/ICMLA.2010.40

M3 - Conference article in proceedings

AN - SCOPUS:79952420541

SN - 9780769543000

SP - 223

EP - 228

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

T2 - IEEE International Conference on Machine Learning and Applications

Y2 - 12 December 2010 through 14 December 2010

ER -

Multiple kernel learning by conditional entropy minimization

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