A survey of the state of the art in learning the kernels
| dc.contributor.author | Abbasnejad, M. | |
| dc.contributor.author | Ramachandram, D. | |
| dc.contributor.author | Mandava, R. | |
| dc.date.issued | 2012 | |
| dc.description.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. | |
| dc.description.statementofresponsibility | M. Ehsan Abbasnejad, Dhanesh Ramachandram, Rajeswari Mandava | |
| dc.identifier.citation | Knowledge and Information Systems, 2012; 31(2):193-221 | |
| dc.identifier.doi | 10.1007/s10115-011-0404-6 | |
| dc.identifier.issn | 0219-1377 | |
| dc.identifier.issn | 0219-3116 | |
| dc.identifier.uri | http://hdl.handle.net/2440/106531 | |
| dc.language.iso | en | |
| dc.publisher | Springer | |
| dc.rights | © Springer-Verlag London Limited 2011 | |
| dc.source.uri | https://doi.org/10.1007/s10115-011-0404-6 | |
| dc.subject | Machine learning; kernel methods; learning the kernels | |
| dc.title | A survey of the state of the art in learning the kernels | |
| dc.type | Journal article | |
| pubs.publication-status | Published |
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