书籍详情
模式识别（英文版.第4版）
作者：（希）西奥多里德斯 等著
出版社：机械工业出版社
出版时间：2009-08-01
ISBN：9787111268963
定价：¥89.00
购买这本书可以去
内容简介
《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。
作者简介
西奥多里德斯，希腊雅典大学信息系教授。主要研究方向是自适应信号处理、通信与模式识别。他是欧洲并行结构及语言协会（PARLE-95）的主席和欧洲信号处理协会（EUSIPCO-98）的常务主席、《信号处理》杂志编委。
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
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
猜您喜欢
