Abstract
Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Keywords
Kernel (algebra)Computer scienceKernel methodField (mathematics)Artificial intelligenceString kernelMachine learningData miningPattern recognition (psychology)Data scienceTheoretical computer scienceRadial basis function kernelSupport vector machineMathematics
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Publication Info
- Year
- 2004
- Type
- book
- Citations
- 6577
- Access
- Closed
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Cite This
John Shawe‐Taylor,
Nello Cristianini
(2004).
Kernel Methods for Pattern Analysis.
Cambridge University Press eBooks
.
https://doi.org/10.1017/cbo9780511809682
Identifiers
- DOI
- 10.1017/cbo9780511809682