@article{cdab1651859743209ab517a165ae404a,
title = "Regularizing multiple kernel learning using response surface methodology",
abstract = "In recent years, several methods have been proposed to combine multiple kernels using a weighted linear sum of kernels. These different kernels may be using information coming from multiple sources or may correspond to using different notions of similarity on the same source. We note that such methods, in addition to the usual ones of the canonical support vector machine formulation, introduce new regularization parameters that affect the solution quality and, in this work, we propose to optimize them using response surface methodology on cross-validation data. On several bioinformatics and digit recognition benchmark data sets, we compare multiple kernel learning and our proposed regularized variant in terms of accuracy, support vector count, and the number of kernels selected. We see that our proposed variant achieves statistically similar or higher accuracy results by using fewer kernel functions and/or support vectors through suitable regularization; it also allows better knowledge extraction because unnecessary kernels are pruned and the favored kernels reflect the properties of the problem at hand.",
keywords = "Multiple kernel learning, Regularization, Response surface methodology, Support vector machine",
author = "Mehmet G{\"o}nen and Ethem Alpaydn",
note = "Funding Information: This work was supported by the Turkish Academy of Sciences in the framework of the Young Scientist Award Program under EA-T{\"U}BA-GEBİP/2001-1-1 , the Boğazi{\c c}i University Scientific Research Project 07HA101 , and the Scientific and Technological Research Council of Turkey (T{\"U}BİTAK) under Grant EEEAG 107E222 . The work of M. G{\"o}nen was supported by the Ph.D. scholarship (2211) from T{\"U}BİTAK. Funding Information: Ethem Alpaydın received his B.Sc. from the Department of Computer Engineering of Boğazi{\c c}i University in 1987 and the degree of Docteur es Sciences from Ecole Polytechnique F{\'e}d{\'e}rale de Lausanne in 1990. He did his postdoctoral work at the International Computer Science Institute, Berkeley, in 1991 and afterwards was appointed as Assistant Professor at the Department of Computer Engineering of Boğazi{\c c}i University. He was promoted to Associate Professor in 1996 and Professor in 2002 in the same department. As visiting researcher, he worked at the Department of Brain and Cognitive Sciences of MIT in 1994, the International Computer Science Institute, Berkeley, in 1997 and IDIAP, Switzerland, in 1998. He was awarded a Fulbright Senior scholarship in 1997 and received the Research Excellence Award from the Boğazi{\c c}i University Foundation in 1998, the Young Scientist Award from the Turkish Academy of Sciences in 2001 and the Scientific Encouragement Award from the Scientific and Technological Research Council of Turkey in 2002. His book Introduction to Machine Learning was published by The MIT Press in October 2004. Its German edition was published in 2008, its Chinese edition in 2009, and its second edition in 2010. Its Turkish edition is in preparation. He is a senior member of the IEEE, an editorial board member of The Computer Journal (Oxford University Press) and an associate editor of Pattern Recognition (Elsevier).",
year = "2011",
month = jan,
doi = "10.1016/j.patcog.2010.07.008",
language = "English (US)",
volume = "44",
pages = "159--171",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier Limited",
number = "1",
}