Shawe-Taylor J., Cristianini N. Kernel Methods for Pattern Analysis

Cambridge University Press, 2004, -478 p.Pattern Analysis is the process of finding general relations in a set of data,
and forms the core of many disciplines, from neural networks to so-called syntactical
pattern recognition, from statistical pattern recognition to machine
learning and data mining. Applications of pattern analysis range from bioinformatics
to document retrieval.
The kernel methodology described here provides a powerful and unified
framework for all of these disciplines, motivating algorithms that can act on
general types of data (e.g. strings, vectors, text, etc.) and look for general
types of relations (e.g. rankings, classifications, regressions, clusters, etc.).
This book fulfils two major roles. Firstly it provides practitioners with a large
toolkit of algorithms, kernels and solutions ready to be implemented, many
given as MatLAB code suitable for many pattern analysis tasks in fields such
as bioinformatics, text analysis, and image analysis. Secondly it furnishes
students and researchers with an easy introduction to the rapidly expanding
field of kernel-based pattern analysis, demonstrating with examples how to
handcraft an algorithm or a kernel for a new specific application, while
covering the required conceptual and mathematical tools necessary to do so.
The book is in three parts. The first provides the conceptual foundations
of the field, both by giving an extended introductory example and by covering
the main theoretical underpinnings of the approach. The second part
contains a number of kernel-based algorithms, from the simplest to sophisticated
systems such as kernel partial least squares, canonical correlation
analysis, support vector machines, principal components analysis, etc. The
final part describes a number of kernel functions, from basic examples to
advanced recursive kernels, kernels derived from generative models such as
HMMs and string matching kernels based on dynamic programming, as well
as special kernels designed to handle text documents.
All those involved in pattern recognition, machine learning, neural networks
and their applications, from computational biology to text analysis
will welcome this account.Part I Basic concepts
Pattern analysis
Kernel methods: an overview
Properties of kernels
Detecting stable patterns
Part II Pattern analysis algorithms
Elementary algorithms in feature space
Pattern analysis using eigen-decompositions
Pattern analysis using convex optimisation
Ranking, clustering and data visualisation
Part III Constructing kernels
Basic kernels and kernel types
Kernels for text
Kernels for structured data: strings, trees, etc.
Kernels from generative models