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 |
Published version: | http://dx.doi.org/10.1007/s10115-011-0404-6 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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