Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/106531
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Type: Journal article
Title: A survey of the state of the art in learning the kernels
Author: Abbasnejad, M.
Ramachandram, D.
Mandava, R.
Citation: Knowledge and Information Systems, 2012; 31(2):193-221
Publisher: Springer
Issue Date: 2012
ISSN: 0219-1377
0219-3116
Statement of
Responsibility: 
M. Ehsan Abbasnejad, Dhanesh Ramachandram, Rajeswari Mandava
Abstract: In recent years, the machine learning community has witnessed a tremendous growth in the development of kernel-based learning algorithms. However, the performance of this class of algorithms greatly depends on the choice of the kernel function. Kernel function implicitly represents the inner product between a pair of points of a dataset in a higher dimensional space. This inner product amounts to the similarity between points and provides a solid foundation for nonlinear analysis in kernel-based learning algorithms. The most important challenge in kernel-based learning is the selection of an appropriate kernel for a given dataset. To remedy this problem, algorithms to learn the kernel have recently been proposed. These methods formulate a learning algorithm that finds an optimal kernel for a given dataset. In this paper, we present an overview of these algorithms and provide a comparison of various approaches to find an optimal kernel. Furthermore, a list of pivotal issues that lead to efficient design of such algorithms will be presented.
Keywords: Machine learning; kernel methods; learning the kernels
Rights: © Springer-Verlag London Limited 2011
DOI: 10.1007/s10115-011-0404-6
Appears in Collections:Aurora harvest 3
Computer Science publications

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