Kernel Methods for Pattern Analysis

Author: John Shawe Taylor
Publisher:
Publication Date: 2005-01-01
Features: Pattern analysis is the process of finding general relationships from a batch of data. It has gradually become the core of many disciplines, from neural networks to what is known as syntactic pattern recognition, from statistical pattern recognition to machine learning and data mining. The applications of pattern analysis cover a wide range of fields, from bioinformatics to document retrieval. The kernel methods described in this book provide a powerful and unified framework for all these disciplines, driving the development of various algorithms that can be used for data of various general forms (such as strings, vectors, text, etc.), and can be used to find various types of general relationships (such as sorting, classification, regression, and clustering). This book has two main purposes. First, it provides professionals with a comprehensive toolbox containing various easily implementable algorithms, kernel functions, and solutions. Many algorithms are provided with MATLAB code, which can be applied to pattern analysis tasks in many fields. Second, it serves as a convenient introductory guide for students and researchers to explore the rapidly developing field of kernel-based pattern analysis. The book illustrates how to manually write an algorithm or kernel function for a new specific application, while also providing preliminary plans and mathematical tools needed to accomplish this task. The book is divided into three parts. Part I introduces the basic concepts of this field, not only providing an expanded introductory example but also elaborating on the main theoretical foundations of this method. Part II includes several kernel-based algorithms, from simple to more complex systems, such as kernel partial order least squares, regularized correlation analysis, support vector machines, principal component analysis, etc. Part III describes several kernel functions, from basic examples to advanced recursive kernel functions, kernel functions derived from generative models (such as HMM), and string matching kernel functions based on dynamic programming, as well as special kernel functions for processing text documents. This book is suitable for all researchers engaged in pattern recognition, machine learning, neural networks, and their applications (from computational biology to text analysis).
Pattern analysis is the process of finding general relationships from a batch of data. It has gradually become the core of many disciplines, from neural networks to what is known as syntactic pattern recognition, from statistical pattern recognition to machine learning and data mining. The applications of pattern analysis cover a wide range of fields, from bioinformatics to document retrieval. The kernel methods described in this book provide a powerful and unified framework for all these disciplines, driving the development of various algorithms that can be used for data of various general forms (such as strings, vectors, text, etc.), and can be used to find various types of general relationships (such as sorting, classification, regression, and clustering). This book has two main purposes. First, it provides professionals with a comprehensive toolbox containing various easily implementable algorithms, kernel functions, and solutions. Many algorithms are provided with MATLAB code, which can be applied to pattern analysis tasks in many fields. Second, it serves as a convenient introductory guide for students and researchers to explore the rapidly developing field of kernel-based pattern analysis. The book illustrates how to manually write an algorithm or kernel function for a new specific application, while also providing preliminary plans and mathematical tools needed to accomplish this task. The book is divided into three parts. Part I introduces the basic concepts of this field, not only providing an expanded introductory example but also elaborating on the main theoretical foundations of this method. Part II includes several kernel-based algorithms, from simple to more complex systems, such as kernel partial order least squares, regularized correlation analysis, support vector machines, principal component analysis, etc. Part III describes several kernel functions, from basic examples to advanced recursive kernel functions, kernel functions derived from generative models (such as HMM), and string matching kernel functions based on dynamic programming, as well as special kernel functions for processing text documents. This book is suitable for all researchers engaged in pattern recognition, machine learning, neural networks, and their applications (from computational biology to text analysis).