统计模式识别：理论与技术概览

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"Statistical Pattern Recognition-2nd edition" 是一本专注于统计模式识别理论与技术的书籍，由Andrew R. Webb撰写。这本书汇集了来自工程、统计、计算机科学和社会科学等多个领域的广泛文献中的内容，主要关注分类和歧视问题。书中详细介绍了当前许多流行的模式处理技术，并涵盖了许多非参数方法在统计领域和其他领域的最新进展。通过实际应用案例来展示这些技术，同时也提供了进一步深入研究应用、比较研究和理论发展的相关文献引用。本书的第一版由Butterworth Heinemann出版，第二版则由John Wiley & Sons Ltd于2002年发布。出版社保留所有权利，未经书面许可，任何部分不得复制或传播。读者可以通过电话、电子邮件或访问出版社网站获取更多信息或购买权限。统计模式识别是机器学习和人工智能领域的一个关键组成部分，它涉及到从数据中识别和学习模式，以便进行有效的分类和预测。在第二版中，Webb博士深入探讨了非参数方法，这是一种不需要对数据分布做出严格假设的统计方法，这使得该书对于处理复杂和非标准数据集特别有用。书中提到的实际应用研究可能包括图像识别、语音识别、生物信息学、金融数据分析等各个领域。通过阅读本书，读者可以了解到如何运用统计学工具来解决实际问题，包括特征选择、模型选择、训练与验证、过拟合和欠拟合的处理策略等。此外，书中还可能涵盖了贝叶斯方法、决策树、支持向量机、神经网络等经典和现代的模式识别技术。Webb博士通过提供实例和指明相关文献，鼓励读者深入探索这个领域的最新发展，这不仅有助于学术研究，也对实际工作中的问题解决具有指导意义。 "Statistical Pattern Recognition-2nd edition" 是一个全面的指南，适合于那些希望深入了解和应用统计模式识别技术的学生、研究人员和从业人员。通过学习本书，读者可以提升对复杂数据的理解能力，掌握处理分类问题的先进方法，从而在各自的领域中取得更好的成果。

Preface

This book provides an introduction to statistical pattern recognition theory and techniques.

Most of the material presented is concerned with discrimination and classiﬁcation and

has been drawn from a wide range of literature including that of engineering, statistics,

computer science and the social sciences. The book is an attempt to provide a concise

volume containing descriptions of many of the most useful of today’s pattern process-

ing techniques, including many of the recent advances in nonparametric approaches to

discrimination developed in the statistics literature and elsewhere. The techniques are

illustrated with examples of real-world applications studies. Pointers are also provided

to the diverse literature base where further details on applications, comparative studies

and theoretical developments may be obtained.

Statistical pattern recognition is a very active area of research. Many advances over

recent years have been due to the increased computational power available, enabling

some techniques to have much wider applicability. Most of the chapters in this book have

concluding sections that describe, albeit brieﬂy, the wide range of practical applications

that have been addressed and further developments of theoretical techniques.

Thus, the book is aimed at practitioners in the ‘ﬁeld’ of pattern recognition (if such

a multidisciplinary collection of techniques can be termed a ﬁeld) as well as researchers

in the area. Also, some of this material has been presented as part of a graduate course

on information technology. A prerequisite is a knowledge of basic probability theory

and linear algebra, together with basic knowledge of mathematical methods (the use

of Lagrange multipliers to solve problems with equality and inequality constraints, for

example). Some basic material is presented as appendices. The exercises at the ends of

the chapters vary from ‘open book’ questions to more lengthy computer projects.

Chapter 1 provides an introduction to statistical pattern recognition, deﬁning some ter-

minology, introducing supervised and unsupervised classiﬁcation. Two related approaches

to supervised classiﬁcation are presented: one based on the estimation of probability

density functions and a second based on the construction of discriminant functions. The

chapter concludes with an outline of the pattern recognition cycle, putting the remaining

chapters of the book into context. Chapters 2 and 3 pursue the density function approach

to discrimination, with Chapter 2 addressing parametric approaches to density estimation

and Chapter 3 developing classiﬁers based on nonparametric schemes.

Chapters 4–7 develop discriminant function approaches to supervised classiﬁcation.

Chapter 4 focuses on linear discriminant functions; much of the methodology of this

chapter (including optimisation, regularisation and support vector machines) is used in

some of the nonlinear methods. Chapter 5 explores kernel-based methods, in particular,

xvi PREFACE

the radial basis function network and the support vector machine, techniques for discrimi-

nation and regression that have received widespread study in recent years. Related nonlin-

ear models (projection-based methods) are described in Chapter 6. Chapter 7 considers a

decision-tree approach to discrimination, describing the classiﬁcation and regression tree

(CART) methodology and multivariate adaptive regression splines (MARS).

Chapter 8 considers performance: measuring the performance of a classiﬁer and im-

proving the performance by classiﬁer combination.

The techniques of Chapters 9 and 10 may be described as methods of exploratory

data analysis or preprocessing (and as such would usually be carried out prior to the

supervised classiﬁcation techniques of Chapters 2–7, although they could, on occasion,

be post-processors of supervised techniques). Chapter 9 addresses feature selection and

feature extraction – the procedures for obtaining a reduced set of variables characterising

the original data. Such procedures are often an integral part of classiﬁer design and it is

somewhat artiﬁcial to partition the pattern recognition problem into separate processes

of feature extraction and classiﬁcation. However, feature extraction may provide insights

into the data structure and the type of classiﬁer to employ; thus, it is of interest in its

own right. Chapter 10 considers unsupervised classiﬁcation or clustering – the process of

grouping individuals in a population to discover the presence of structure; its engineering

application is to vector quantisation for image and speech coding.

Finally, Chapter 11 addresses some important diverse topics including model selec-

tion. Appendices largely cover background material and material appropriate if this book

is used as a text for a ‘conversion course’: measures of dissimilarity, estimation, linear

algebra, data analysis and basic probability.

The website www.statistical-pattern-recognition.net contains refer-

ences and links to further information on techniques and applications.

In preparing the second edition of this book I have been helped by many people.

I am grateful to colleagues and friends who have made comments on various parts of

the manuscript. In particular, I would like to thank Mark Briers, Keith Copsey, Stephen

Luttrell, John O’Loghlen and Kevin Weekes (with particular thanks to Keith for examples

in Chapter 2); Wiley for help in the ﬁnal production of the manuscript; and especially

Rosemary for her support and patience.

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