模式识别（英文版·第3版）

　　本书综合考虑了有监督和无监督模式识别的经典的以及当前的理论和实践，为专业技术人员和高校学生建立起了完整的基本知识体系。本书由模式识别领域内的两位顶级专家合著，从工程角度全面阐述了模式识别的应用，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。本书是享誉世界的名著，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。本书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。● 提供了最新的关于支持向量机的研究成果 （包括V-SVM及其几何解释）。● 讨论了多分类器组合方法 （包括Boosting方法）。● 增加了最新的资料。介绍了一些聚类算法，这些算法根据Web挖掘和生物信息等应用的要求而修改，以适合大数据集和高维数据。● 涵盖了不同的应用，例如图像分析、光学字符识别、信道均衡、语音识别和音频分类等。● 面向服务的软件工程，解释了如何将可复用的Web服务用于开发新的应用。● 面向方面的软件开发，描述了基于关注点分离的新技术。

　　本书提供作译者介绍Sergios Theodoridis是希腊雅典大学信息与通信系教授。他于1973年在雅典大学获得物理学学士学位，又分别于1975年和1978年在英国伯明翰大学获得信号处理与通信硕士和博士学位。他的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。Konstantinos Koutroumbas拥有雅典大学博士学位，任职于希腊雅典国家天文台空间应用与遥感研究院，是国际知名的专家。

preface
chapter 1 introduction
1.1 is pattern recognition important?
1.2 features, feature vectors, and classifiers
1.3 supervised versus unsupervised pattern
recognition
1.4 outline of the book
chapter 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
2.5.1 maximum likelihood parameter estimation
2.5.2 maximum a posteriori probability
estimation
2.5.3 bayesian inference
2.5.4 maximum entropy estimation
2.5.5 mixture models
2.5.6 nonparametric estimation
2.6 the nearest neighbor rule
chapter 3 linear classifiers
3.1 introduction
3.2 linear discriminant functions and decision
hyperplanes
3.3 the perceptron algorithm
3.4 least squares methods
3.4.1 mean square error estimation
3.4.2 stochastic approximation and the lms
algorithm
3.4.3 sum of error squares estimation
3.5 mean square estimation revisited
3.5.1 mean square error regression
3.5.2 mse estimates posterior class probabilities
3.5.3 the bias-variance dilemma
3.6 support vector machines
3.6.1 separable classes
3.6.2 nonseparable classes
chapter 4 nonlinear classifiers
4.1 introduction
4.2 the xor problem
4.3 the two-layer perceptron
4.3.1 classification capabilities of the two-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 in linear
dichotomies
4.14 polynomial classifiers
4.15 radial basis function networks
4.16 universal approximators
4.17 support vector machines: the nonlinear case
4.18 decision trees
4.18.1 set of questions
4.18.2 splitting criterion
4.18.3 stop-splitting rule
4.18.4 class assignment rule
4.19 discussion
chapter 5 feature selection
5.1 introduction
5.2 preprocessing
5.2.1 outlier removal
5.2.2 data normalization
5.2.3 missing data
5.3 feature selection based on statistical hypothesis
testing
5.3.1 hypothesis testing basics
5.3.2 application of the t-test in feature
selection
5.4 the receiver operating characteristics croc curve
5.5 class separability measures
5.5.1 divergence
5.5.2 chernoff bound and
bhattacharyya distance
5.5.3 scatter matrices
5.6 feature subset selection
5.6.1 scalar feature selection
5.6.2 feature vector selection
5.7 optimal feature generation
5.8 neural networks and feature generation/selection
5.9 a hint on the vapnik--chemovenkis learning
theory
chapter 6 feature generation i: linear transforms
6.1 introduction
6.2 basis vectors and images
6.3 the karhunen-loeve transform
6.4 the singular value decomposition
6.5 independent component analysis
6.5.1 ica based on second- and fourth-order
cumulants
6.5.2 ica based on mutual information
6.5.3 an ica simulation example
6.6 the discrete fourier transform (dft)
6.6.1 one-dimensional dft
6.6.2 two-dimensional dft
6.7 the discrete cosine and sine transforms
6.8 the hadamard transform
6.9 the haar transform
6.10 the haar expansion revisited
6.11 discrete time wavelet transform (dtwt)
6.12 the multiresolution interpretation
6.13 wavelet packets
6.14 a look at two-dimensional generalizations
6.15 applications
chapter 7 feature generation ii
7.1 introduction
7.2 regional features
7.2.1 features for texture characterization
7.2.2 local linear transforms for texture
feature extraction
7.2.3 moments
7.2.4 parametric models
7.3 features for shape and size characterization
7.3.1 fourier features
7.3.2 chain codes
7.3.3 moment-based features
7.3.4 geometric features
7.4 a glimpse at fractals
7.4.1 self-similarity and fractal dimension
7.4.2 fractional brownian motion
chapter 8 template matching
8.1 introduction
8.2 measures based on optimal path searching
techniques
8.2.1 bellman's optimality principle and
dynamic programming
8.2.2 the edit distance
8.2.3 dynamic time warping in speech
recognition
8.3 measures based on correlations
8.4 deformable template models
chapter 9 context-dependent classification
9.1 introduction
9.2 the bayes classifier
9.3 markov chain models
9.4 the viterbi algorithm
9.5 channel equalization
9.6 hidden markov models
9.7 training markov models via neural networks
9.8 a discussion of markov random fields
chaptsr 10 system evaluation
10.1 introduction
10.2 error counting approach
10.3 exploiting the finite size of the data set
10.4 a case study from medical imaging
chapter 11 clustering: basic concepts
11.1 introduction
11.1.1 applications of cluster analysis
11.1.2 types of features
11.1.3 definitions of clustering
11.2 proximity measures
11.2.1 definitions
11.2.2 proximity measures between two points
11.2.3 proximity functions between a point and
a set
11.2.4 proximity functions between two sets
chapter 12 clustering algorithms i: sequential
algorithms
12.1 introduction
12.1.1 number of possible clusterings
12.2 categories of clustering algorithms
12.3 sequential clustering algorithms
12.3.1 estimation of the number of clusters
12.4 a modification of bsas
12.5 a two-threshold sequential scheme
12.6 refinement stages
12.7 neural network implementation
12.7.1 description of the architecture
12.7.2 implementation of the bsas algorithm
chapter 13 clustering algorithms ii: hierarchical
algorithms
13.1 introduction
13.2 agglomerative algorithms
13.2.1 definition of some useful quantities
13.2.2 agglomerative algorithms based on
matrix thetry
13.2.3 monotonicity and crossover
13.2.4 implementational issues
13.2.5 agglomerative algorithms based on
graph theory
13.2.6 ties in the proximity matrix
13.3 the cophenetic matrix
13.4 divisive algorithms
13.5 choice of the best number of clusters
chapter 14 clustering algorithms iii:
schemes based on function optimization
14.1 introduction
14.2 mixture decomposition schemes
14.2.1 compact and hyperellipsoidal clusters
14.2.2 a geometrical interpretation
14.3 fuzzy clustering algorithms
14.3.1 point representatives
14.3.2 quadric surfacesas representatives
14.3.3 hyperplane representatives
14.3.4 combining quadric and hyperplane
representatives
14.3.5 a geometrical interpretation
14.3.6 convergence aspects of the fuzzy
clustering algorithms
14.3.7 alternating cluster estimation
14.4 possibilistic clustering
14.4.1 the mode-seeking property
14.4.2 an alternative possibilistic scheme
14.5 hard clustering algorithms
14.5.1 the isodata or k-means or c-means
algorithm
14.6 vector quantization
chapter 15 clustering algorithms iv
15.1 introduction
15.2 clustering algorithms based on graph theory
15.2.1 minimum spanning tree algorithms
15.2.2 algorithms based on regions of influence
15.2.3 algorithms based on directed trees
15.3 competitive learning algorithms
15.3.1 basic competitive learning algorithm
15.3.2 leaky learning algorithm
15.3.3 conscientious competitive learning
algorithms
15.3.4 competitive learning-like algorithms
associated with cost functions
15.3.5 self-organizing maps
15.3.6 supervised learning vector quantization
15.4 branch and bound clustering algorithms
15.5 binary morphology clustering algorithms (bmcas)
15.5.1 discretization
15.5.2 morphological operations
15.5.3 determination of the clusters in a discrete
binary set
15.5.4 assignment of feature vectors to clusters
15.5.5 the algorithmic scheme
15.6 boundary detection algorithms
15.7 valley-seeking clustering algorithms
15.8 clustering via cost optimization (revisited)
15.8.1 simulated annealing
15.8.2 deterministic annealing
15.9 clustering using genetic algorithms
15.10 other clustering algorithms
chapter 16 cluster validity
16.1 introduction
16.2 hypothesis testing revisited
16.3 hypothesis testing in cluster validity
16.3.1 external criteria
16.3.2 internal criteria
16.4 relative criteria
16.4.1 hard clustering
16.4.2 fuzzy clustering
16.5 validity of individual clusters
16.5.1 external criteria
16.5.2 internal criteria
16.6 clustering tendency
16.6.1 tests for spatial randomness
appendix a
hints from probability and statistics
appendix b
linear algebra basics
appendix c
cost function optimization
appendix d
basic definitions from linear systems theory
index