John Shawe-Taylor & Nello Cristianini - Cambridge University Press, 2004 - Ordering Info »
Kernel Methods for Pattern Analysis
Welcome to the website for the book Kernel Methods for Pattern Analysis.
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.