Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/106531
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dc.contributor.authorAbbasnejad, M.-
dc.contributor.authorRamachandram, D.-
dc.contributor.authorMandava, R.-
dc.date.issued2012-
dc.identifier.citationKnowledge and Information Systems, 2012; 31(2):193-221-
dc.identifier.issn0219-1377-
dc.identifier.issn0219-3116-
dc.identifier.urihttp://hdl.handle.net/2440/106531-
dc.description.abstractIn 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.-
dc.description.statementofresponsibilityM. Ehsan Abbasnejad, Dhanesh Ramachandram, Rajeswari Mandava-
dc.language.isoen-
dc.publisherSpringer-
dc.rights© Springer-Verlag London Limited 2011-
dc.subjectMachine learning; kernel methods; learning the kernels-
dc.titleA survey of the state of the art in learning the kernels-
dc.typeJournal article-
dc.identifier.doi10.1007/s10115-011-0404-6-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 3
Computer Science publications

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