Kernel Methods for Pattern Analysis

This book fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems in field 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. 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 machine learning to 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 toolk it 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 hand-craft 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. 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. Annotation This book provides professionals with a large selection of algorithms, kernels and solutions ready for implementation and suitable for standard pattern discovery problems in fields such as bioinformatics, text analysis and image analysis. It also serves as an 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 The kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems