Kernel Methods for Pattern Analysis
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 syn-
tactical pattern recognition, from statistical pattern recognition to machine
learning and data mining. Applications of pattern analysis range from bioin-
formatics 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 cov-
ering the main theoretical underpinnings of the approach. The second part
contains a number of kernel-based algorithms, from the simplest to sophis-
ticated 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 net-
works and their applications, from computational biology to text analysis
will welcome this account.
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