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- Publisher
- Springer Journals
- Copyright
- Copyright © 2012 by Springer Science+Business Media Dordrecht
- Subject
- Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
- ISSN
- 0269-2821
- eISSN
- 1573-7462
- DOI
- 10.1007/s10462-012-9369-4
- Publisher site
- See Article on Publisher Site
Abstract
The success of kernel methods is very much dependent on the choice of kernel. Kernel design and learning a kernel from the data require evaluation measures to assess the quality of the kernel. In recent years, the notion of kernel alignment, which measures the degree of agreement between a kernel and a learning task, is widely used for kernel selection due to its effectiveness and low computational complexity. In this paper, we present an overview of the research progress of kernel alignment and its applications. We introduce the basic idea of kernel alignment and its theoretical properties, as well as the extensions and improvements for specific learning problems. The typical applications, including kernel parameter tuning, multiple kernel learning, spectral kernel learning and feature selection and extraction, are reviewed in the context of classification framework. The relationship between kernel alignment and other evaluation measures is also explored. Finally, concluding remarks and future directions are presented.
Journal
Artificial Intelligence Review – Springer Journals
Published: Nov 8, 2012