统计模式识别：新兴应用与技术进展

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统计模式识别(Statistical Pattern Recognition)是当前科研领域的热点，其发展迅速，特别是在数据挖掘、网络搜索、多媒体数据检索、人脸识别以及手写体识别等新兴应用方面。这些领域对模式识别技术的需求越来越高，不仅要具备强大的鲁棒性，还要有高效的执行能力。该领域的研究基础在于统计决策和估计，它们被视为理解并实现模式识别的关键基石。统计模式识别第二版由Andrew R. Webb撰写，由QinetiQ公司在英国Malvern出版，原著曾由Butterworth-Heinemann公司发行。版权归属于2002年的John Wiley & Sons Ltd。该书的联系方式包括电话、电子邮件和网站，强调所有权利保留，未经出版商书面许可，不得任何形式复制、存储、传输或通过电子、机械、影印、录音、扫描等方式进行复制。本书详细探讨了统计方法在模式识别中的应用，内容涵盖了理论基础、算法设计、模型构建以及实际案例分析。它可能包括概率论、贝叶斯分类、最大似然估计、支持向量机、神经网络等统计学习工具，以及如何利用这些工具处理复杂的数据集，解决实际问题中的模式识别挑战。书中还会介绍模式识别的评估指标，如准确率、召回率、F1分数等，以及如何通过交叉验证和调参优化模型性能。对于机器学习和人工智能专业人员，这是一本深入理解并掌握统计模式识别核心原理和技术的重要参考书籍。此外，书中还可能涉及最新的研究成果，如深度学习驱动的模式识别、集成学习方法（如随机森林、梯度提升机）的应用，以及在大数据背景下，如何处理高维稀疏数据和大规模流式数据的策略。 "Statistical Pattern Recognition"是一本既理论性强又实用的指南，适合那些希望在当今信息技术时代深入理解并应用模式识别技术的专业人士，无论是初学者还是经验丰富的研究人员都能从中获益良多。

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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统计模式识别：新兴应用与技术进展

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如何用matlab GUI设计并实现杂草识别系统并包括图像预处理、特征提取、分类识别以及结果展示等功能的代码是？