模式识别

模式识别

(希) 西奥多里德斯 (Theodoridis,S.) 等, 著

出版社:机械工业出版社

年代:2009

定价:60.0

书籍简介:

本书内容包括贝叶斯分类、贝叶斯网络、线性和非线性分类器(包含神经网络和支持向量机)、动态编程和用于顺序数据的隐马尔科夫模型、特征生成(包含小波、主成分分析、独立成分分析和分形分析)、特征选择技术、来自学习理论的基本概念、聚类概念和算法等。

书籍目录:

Preface

CHAPTER1 Introduction

1.1 Is Pattern Recognition Important?

1.2 Features, Feature Vectors, and Classifiers

1.3 Supervised, Unsupervised, and Semi-Supervised Learning

1.4 MATLAB Programs

1.5 Outline of The Book

CHAPTER2 Classifiers Based on Bayes Decision Theory

2.1 Introduction

2.2 Bayes Decision Theory

2.3 Discriminant Functions and Decision Surfaces

2.4 Bayesian Classification for Normal Distributions

2.5 Estimation of Unknown Probability Density Functions

2.6 The Nearest Neighbor Rule

2.7 Bayesian Networks

2.8 Problems

References

CHAPTER3 Linear Classifiers

3.1 Introduction

3.2 Linear Discriminant Functions and Decision Hyperplanes

3.3 The Perceptron Algorithm

3.4 Least Squares Methods

3.5 Mean Square Estimation Revisited

3.6 Logistic Discrimination

3.7 Support Vector Machines

3.8 Problems

References

CHAPTER 4 Nonlinear Classifiers

4.1 Introduction

4.2 The XOR Problem

4.3 TheTwo-Layer Perceptron

4.4 Three-Layer Perceptrons

4.5 Algorithms Based on Exact Classification of the Training Set

4.6 The Backpropagation Algorithm

4.7 Variations on the Backpropagation Theme

4.8 The Cost Function Choice

4.9 Choice of the Network Size

4.10 A Simulation Example

4.11 Networks with Weight Sharing

4.12 Generalized Linear Classifiers

4.13 Capacity of the/-Dimensional Space inLinear Dichotomies

4.14 Polynomial Classifiers

4.15 Radial Basis Function Networks

4.16 UniversalApproximators

4.17 Probabilistic Neural Networks

4.18 Support Vector Machines: The Nonlinear Case

4.19 Beyond the SVM Paradigm

4.20 Decision Trees

4.21 Combining Classifiers

4.22 The Boosting Approach to Combine Classifiers

4.23 The Class Imbalance Problem

4.24 Discussion

4.25 Problems

References

CHAPTER5 Feature Selection

5.1 Introduction

5.2 Preprocessing

5.3 The Peaking Phenomenon

5.4 Feature Selection Based on Statistical Hypothesis Testing

5.5 The Receiver Operating Characteristics (ROC) Curve

5.6 Class Separability Measures

5.7 Feature Subset Selection

5.8 Optimal Feature Generation

5.9 Neural Networks and Feature Generation/Selection

5.10 A Hint On Generalization Theory

5.11 The Bayesian Information Criterion

5.12 Problems

References

CHAPTER 6 FEATURE GENERATION Ⅰ:LINEAR TRANSFORMS

CHAPTER 7 FEATURE GENERATION Ⅱ

CHAPTER 8 TEMPLATE MATCHING

CHAPTER 9 CONTEXT-DEPENDENT CLASIFICATION

CHAPTER10 SYSTEM EVALUATION

CHAPTER11 CLUSTERING:BASIC CONCEPTS

CHAPTER12 CLUSTERING ALGORITHMSⅠ:SEQUENTIAL ALGORITHMS

CHAPTER13 CLUSTERING ALGORITHMSⅡ:HIERARCHICAL ALGORITHMS

CHAPTER14 CLUSTERING ALGORITHMSⅢ:SCHEMES BASED ON FUNCTION OPTIMIZATION

CHAPTER15 CLUSTERING ALGORITHMSⅣ

CHAPTER16 CLUSTER VALIDITY

Appendix A Hints form Probability and Statistics

Appendix B Linear Algebra Basics

内容摘要:

《模式识别(英文版)(第4版)》是享誉世界的名著,内容既全面又相对独立,既有基础知识的介绍,又有本领域研究现状的介绍,还有对未来发展的展望,是本领域最全面的参考书,被世界众多高校选用为教材。《模式识别(英文版)(第4版)》可作为高等院校计算机。电子、通信。自动化等专业研究生和高年级本科生的教材,也可作为计算机信息处理、自动控制等相关领域的工程技术人员的参考用书。《模式识别(英文版)(第4版)》主要特点提供了大型数据集和高维数据的聚类算法以及网络挖掘和生物信息学应用的最新资料。涵盖了基于图像分析、光学字符识别,信道均衡,语音识别和音频分类的多种应用。呈现了解决分类和稳健回归问题的内核方法取得的最新成果。介绍了带有Boosting方法的分类器组合技术。提供更多处理过的实例和图例,加深读者对各种方法的了解。增加了关于热点话题的新的章节,包括非线性维数约减、非负矩阵分解、实用性反馈。稳健回归、半监督学习,谱聚类和聚类组合技术。

书籍规格:

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9787111268963
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出版地北京出版单位机械工业出版社
版次1版印次1
定价(元)60.0语种英文
尺寸19装帧平装
页数 977 印数 3000

书籍信息归属:

模式识别是机械工业出版社于2009.05出版的中图分类号为 TP391.4 的主题关于 模式识别-英文 的书籍。