Pattern Classification,9780471056690

Pattern Classification

by ; ;
Edition: 2nd
ISBN13: 9780471056690
ISBN10: 0471056693
Format: Hardcover
Pub. Date: 2000-11-09
Publisher(s): Wiley-Interscience
List Price: $196.95

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Summary

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Author Biography

RICHARD O. DUDA, PhD, is Professor in the Electrical Engineering Department at San Jose State University, San Jose, California. <P>PETER E. HART, PhD, is Chief Executive Officer and President of Ricoh Innovations, Inc. in Menlo Park, California. <P>DAVID G. STORK, PhD, is Chief Scientist, also at Ricoh Innovations, Inc.

Table of Contents

Preface xvii
Introduction
1(19)
Machine Perception
1(1)
An Example
1(8)
Related Fields
8(1)
Pattern Recognition Systems
9(5)
Sensing
9(1)
Segmentation and Grouping
9(2)
Feature Extraction
11(1)
Classification
12(1)
Post Processing
13(1)
The Design Cycle
14(2)
Data Collection
14(1)
Feature Choice
14(1)
Model Choice
15(1)
Training
15(1)
Evaluation
15(1)
Computational Complexity
16(1)
Learning and Adaptation
16(1)
Supervised Learning
16(1)
Unsupervised Learning
17(1)
Reinforcement Learning
17(1)
Conclusion
17(3)
Summary by chapters
17(1)
Bibliographical and Historical Remarks
18(1)
Bibliography
19(1)
Bayesian Decision Theory
20(64)
Introduction
20(4)
Bayesian Decison Theory---Continuous Features
24(2)
Two-Category Classification
25(1)
Minimum-error-Rate Classification
26(3)
Minimax Criterion
27(1)
Neyman-Pearson Criterion
28(1)
Classifiers, Discriminant Functions, and Decision Surfaces
29(2)
The Multicategory Case
29(1)
The Two-Category Case
30(1)
The Normal Density
31(5)
Univariate Density
32(1)
Multivariate Density
33(3)
Discriminant Functions for the Normal Density
36(9)
Case 1: Σi = σ2I
36(3)
Case 2: Σi = Σ
39(2)
Case 3: Σi = arbitrary
41(1)
Decision Regions for Two-Dimensional Gaussian Date
41(4)
Error Probabilities and Integrals
45(1)
Error Bounds for Normal Densities
46(5)
Chernoff Bound
46(1)
Bhattacharyya Bound
47(1)
Error Bounds for Gaussian Distribution
48(1)
Signal Detection Theory and Operating Characteristics
48(3)
Bayes Decision Theory---Discrete Features
51(3)
Independent Binary Features
52(1)
Bayesian Decisions for Three-Dimensional Binary Data
53(1)
Missing and Noisy Features
54(2)
Missing Features
54(1)
Noisy Features
55(1)
Bayesian Belief Network
56(6)
Belief Network for Fish
59(3)
Compound Bayesian Decision Theory and Context
62(22)
Summary
63(1)
Bibliographical and Historical Remarks
64(1)
Problems
65(15)
Computer exercises
80(2)
Bibliography
82(2)
Maximum-Likelihood and Bayesian Parameter Estimation
84(77)
Introduction
84(1)
Maximum-Likelihood Estimation
85(5)
The General Principle
85(3)
The Gaussian Case: Unknown μ
88(1)
The Gaussian Case: Unknown μ and Σ
88(1)
Bias
89(1)
Bayesian Estimation
90(2)
The Class-Conditional Densities
91(1)
The Parameter Distribution
91(1)
Bayesian Parameter Estimation: Gaussian Case
92(5)
The Univariate Case: p(μ\D)
92(3)
The Univariate Case: p(x\D)
95(1)
The Multivariate Case
95(2)
Bayesian Parameter Estimation: General Theory
97(5)
Recursive Bayes Learning
98(2)
When Do Maximum-Likelihood and bayes Methods Differ?
100(1)
Noninformative Priors and Invariance
101(1)
Gibbs Algorithm
102(1)
Sufficient Statistics
102(5)
Sufficient Statistics and the Exponential Family
106(1)
Problems of Dimensionality
107(7)
Accuracy Dimension and Training Sample Size
107(4)
Computational Complexity
111(2)
Overfitting
113(1)
Component Analysis and Discriminants
114(10)
Principal Component Analysis (PCA)
115(2)
Fisher Linear Discriminant
117(4)
Multiple Discriminant Analysis
121(3)
Expectation-Maximization (EM)
124(4)
Expectation-Maximization for a 2D Normal Model
126(2)
Hidden Markov Models
128(33)
First-Order Markov Models
128(1)
First-Order Hidden Markov Models
129(1)
Hidden Markov Model Computation
129(2)
Evaluation
131(2)
Hidden Markov Model
133(2)
Decoding
135(1)
HMM Decoding
136(1)
Learning
137(2)
Summary
139(1)
Bibliographical and Historical Remarks
139(1)
Problems
140(15)
Computer exercises
155(4)
Bibliography
159(2)
Nonparametric Techniques
161(54)
Introduction
161(1)
Density Estimation
161(3)
Parzen Windows
164(10)
Convergence of the Mean
167(1)
Convergence of the Variance
167(1)
Illustrations
168(1)
Classification Example
168(4)
Probabilistic Neural Networks (PNNs)
172(2)
Choosing the Window Function
174(1)
kn-Nearest-Neighbor Estimation
174(3)
kn-Nearest-Neighbor and Parzen-Window Estimation
176(1)
Estimation of A Posteriori Probabilities
177(1)
The Nearest-Neighbor Rule
177(10)
Convergence of the Nearest Neighbor
179(1)
Error Rate for the Nearest-Neighbor Rule
180(1)
Error Bounds
180(2)
The k-Nearest-Neighbor Rule
182(2)
Computational Complexity of the k-Nearest-Neighbor Rule
184(3)
Metrics and Nearest-Neighbor Classification
187(5)
Properties of Metrics
187(1)
Tangent Distance
188(4)
Fuzzy Classification
192(3)
Reduced Coulomb Energy Networks
195(2)
Approximations by Series Expansions
197(18)
Summary
199(1)
Bibliographical and Historical Remarks
200(1)
Problems
201(8)
Computer exercises
209(4)
Bibliography
213(2)
Linear Discriminant Functions
215(67)
Introduction
215(1)
Linear Discriminant Functions and Decision Surfaces
216(3)
The Two-Category Case
216(2)
The Multicategory Case
218(1)
Generalized Linear Discriminant Functions
219(4)
The Two-Category Linearly Separable Case
223(4)
Geometry and Terminology
224(1)
Gradient Descent Procedures
224(3)
Minimizing the Perceptron Criterion Function
227(8)
The Perceptron Criterion Function
227(2)
Convergence Proof for Single-Sample Correction
229(3)
Some Direct Generalizations
232(3)
Relaxation Procedures
235(3)
The Descent Algorithm
235(2)
Convergence Proof
237(1)
Nonseparable Behavior
238(1)
Minimum Squared-Error Procedures
239(10)
Minimum Squared-Error and the Pseudoinverse
240(1)
Constructing a Linear Classifier by Matrix Pseudoinverse
241(1)
Relation to Fisher's Linear Discriminant
242(1)
Asymptotic Approximation to an Optimal Discriminant
243(2)
The Widrow-Hoff or LMS Procedure
245(1)
Stochastic Approximation Methods
246(3)
The Ho-Kashyap Procedures
249(7)
The Descent Procedure
250(1)
Convergence Proof
251(2)
Nonseparable Behavior
253(1)
Some Related Procedures
253(3)
Linear Programming Algorithms
256(3)
Linear Programming
256(1)
The Linearly Separable Case
257(1)
Minimizing the Perceptron Criterion Function
258(1)
Support Vector Machines
259(6)
SVM Training
263(1)
SVM for the XOR Problem
264(1)
Multicategory Generalizations
265(17)
Kesler's Construction
266(1)
Convergence of the Fixed-Increment Rule
266(2)
Generalizations for MSE Procedures
268(1)
Summary
269(1)
Bibliographical and Historical Remarks
270(1)
Problems
271(7)
Computer exercises
278(3)
Bibliography
281(1)
Multilayer Neural Networks
282(68)
Introduction
282(2)
Feedforward Operation and Classification
284(4)
General Feedforward Operation
286(1)
Expressive Power of Multilayer Networks
287(1)
Backpropagation Algorithm
288(8)
Network Learning
289(4)
Training Protocols
293(2)
Learning Curves
295(1)
Error Surfaces
296(3)
Some Small Networks
296(2)
The Exclusive-OR (XOR)
298(1)
Larger Networks
298(1)
How Important Are Multiple Minima?
299(1)
Backpropagation as Feature Mapping
299(4)
Representations at the Hidden Layer-Weights
302(1)
Backpropagation, Bayes Theory and Probability
303(2)
Bayes Discriminants and Neural Networks
303(1)
Outputs as Probabilities
304(1)
Related Statistical Techniques
305(1)
Practical Techniques for Improving Backpropagation
306(12)
Activation Function
307(1)
parameters for the Sigmoid
308(1)
Scaling Input
308(1)
Target Values
309(1)
Training with Noise
310(1)
Manufacturing Data
310(1)
Number of Hidden Units
310(1)
Initializing Weights
311(1)
Learning Rates
312(1)
Momentum
313(1)
Weight Decay
314(1)
Hints
315(1)
On-Line, Stochastic or Batch Training?
316(1)
Stopped Training
316(1)
Number of Hidden Layers
317(1)
Criterion Function
318(1)
Second-Order Methods
318(6)
Hessian Matrix
318(1)
Newton's Method
319(1)
Quickprop
320(1)
Conjugate Gradient Descent
321(1)
Conjugate Gradient Descent
322(2)
Additional Networks and Training Methods
324(6)
Radial Basis Function Networks (RBFs)
324(1)
Special Bases
325(1)
Matched Filters
325(1)
Convolutional Networks
326(2)
Recurrent Networks
328(1)
Cascade-Correlation
329(1)
Regularization, Complexity Adjustment and Pruning
330(20)
Summary
333(1)
Bibliographical and Historical Remarks
333(2)
Problems
335(8)
Computer exercises
343(4)
Bibliography
347(3)
Stochastic Methods
350(44)
Introduction
350(1)
Stochastic Search
351(9)
Simulated Annealing
351(1)
The Boltzmann Factor
352(5)
Deterministic Simulated Annealing
357(3)
Boltzmann Learning
360(10)
Stochastic Boltzmann Learning of Visible States
360(5)
Missing Features and Category Constraints
365(1)
Deterministic Boltzmann Learning
366(1)
Initialization and Setting Parameters
367(3)
Boltzmann Networks and Graphical Models
370(3)
Other Graphical Models
372(1)
Evolutionary Methods
373(5)
Genetic Algorithms
373(4)
Further Heuristics
377(1)
Why Do They Work?
378(1)
Genetic Programming
378(16)
Summary
381(1)
Bibliographical and Historical Remarks
381(2)
Problems
383(5)
Computer exercises
388(3)
Bibliography
391(3)
Nonmetric Methods
394(59)
Introduction
394(1)
Decision Trees
395(1)
CART
396(15)
Number of Splits
397(1)
Query Selection and Node Impurity
398(4)
When to Stop Splitting
402(1)
Pruning
403(1)
Assignment of Leaf Node Labels
404(1)
A Simple Tree
404(2)
Computational Complexity
406(1)
Feature Choice
407(1)
Multivariate Decision Trees
408(1)
Priors and Costs
409(1)
Missing Attributes
409(1)
Surrogate Splits and Missing Attributes
410(1)
Other Tree Methods
411(2)
ID3
411(1)
C4.5
411(1)
Which Tree Classifier Is Best?
412(1)
Recognition with Strings
413(8)
String Matching
415(3)
Edit Distance
418(2)
Computational Complexity
420(1)
String Matching with Errors
420(1)
String Matching with the ``Don't-Care'' Symbol
421(1)
Grammatical Methods
421(8)
Grammars
422(2)
Types of String Grammars
424(1)
A Grammar for Pronouncing Numbers
425(1)
Recognition Using Grammars
426(3)
Grammatical Inference
429(2)
Grammatical Inference
431(1)
Rule-Based Methods
431(22)
Learning Rules
433(1)
Summary
434(1)
Bibliographical and Historical Remarks
435(2)
Problems
437(9)
Computer exercises
446(4)
Bibliography
450(3)
Algorithm-Independent Machine Learning
453(64)
Introduction
453(1)
Lack of Inherent Superiority of Any Classifier
454(11)
No Free Lunch Theorem
454(3)
No Free Lunch for Binary Data
457(1)
Ugly Duckling Theorem
458(3)
Minimum Description Length (MDL)
461(2)
Minimum Description Length Principle
463(1)
Overfitting Avoidance and Occam's Razor
464(1)
Bias and Variance
465(6)
Bias and Variance for Regression
466(2)
Bias and Variance for Classification
468(3)
Resampling for Estimating Statistics
471(4)
Jackknife
472(1)
Jackknife Estimate of Bias and Variance of the Mode
473(1)
Bootstrap
474(1)
Resampling for Classifier Design
475(7)
Bagging
475(1)
Boosting
476(4)
Learning with Queries
480(2)
Arcing, Learning with Queries, Bias and Variance
482(1)
Estimating and Comparing Classifiers
482(13)
Parametric Models
483(1)
Cross-Validation
483(2)
Jackknife and Bootstrap Estimation of Classification Accuracy
485(1)
Maximum-Likelihood Model Comparison
486(1)
Bayesian Model Comparison
487(2)
The Problem-Average Error Rate
489(3)
Predicting Final Performance from Learning Curves
492(2)
The Capacity of a Separating Plane
494(1)
Combining Classifiers
495(22)
Component Classifiers with Discriminant Functions
496(2)
Component Classifiers without Discriminant Functions
498(1)
Summary
499(1)
Bibliographical and Historical Remarks
500(2)
Problems
502(6)
Computer exercises
508(5)
Bibliography
513(4)
Unsupervised Learning and Clustering
517(84)
Introduction
517(1)
Mixture Densities and Identifiability
518(1)
Maximum-Likelihood Estimates
519(2)
Application to Normal Mixtures
521(9)
Case 1: Unknown Mean Vectors
522(2)
Case 2: All Parameters Unknown
524(2)
k-Means Clustering
526(2)
Fuzzy k-Means Clustering
528(2)
Unsupervised Bayesian Learning
530(7)
The Bayes Classifier
530(1)
Learning the Parameter Vector
531(3)
Unsupervised Learning of Gaussian Data
534(2)
Decision-Directed Approximation
536(1)
Data Description and Clustering
537(5)
Similarity Measures
538(4)
Criterion Functions for Clustering
542(6)
The Sum-of-Squared-Error Criterion
542(1)
Related Minimum Variance Criteria
543(1)
Scatter Criteria
544(2)
Clustering Criteria
546(2)
Iterative Optimization
548(2)
Hierarchical Clustering
550(7)
Definitions
551(1)
Agglomerative Hierarchical Clustering
552(3)
Stepwise-Optimal Hierarchical Clustering
555(1)
Hierarchical Clustering and Induced Metrics
556(1)
The Problem of Validity
557(2)
On-line clustering
559(7)
Unknown Number of Clusters
561(2)
Adaptive Resonance
563(2)
Learning with a Critic
565(1)
Graph-Theoretic Methods
566(2)
Component Analysis
568(5)
Principal Component Analysis (PCA)
568(1)
Nonlinear Component Analysis (NLCA)
569(1)
Independent Component Analysis (ICA)
570(3)
Low-Dimensional Representations and Multidimensional Scaling (MDS)
573(28)
Self-Organizing Feature Maps
576(4)
Clustering and Dimensionality Reduction
580(1)
Summary
581(1)
Bibliographical and Historical Remarks
582(1)
Problems
583(10)
Computer exercises
593(5)
Bibliography
598(3)
A MATHEMATICAL FOUNDATIONS 601(36)
A.1 Notation
601(3)
A.2 Linear Algebra
604(6)
A.2.1 Notation and Preliminaries
604(1)
A.2.2 Inner Product
605(1)
A.2.3 Outer Product
606(1)
A.2.4 Derivatives of Matrices
606(2)
A.2.5 Determinant and Trace
608(1)
A.2.6 Matrix Inversion
609(1)
A.2.7 Eigenvectors and Eigenvalues
609(1)
A.3 Lagrange Optimization
610(1)
A.4 Probability Theory
611(12)
A.4.1 Discrete Random Variables
611(1)
A.4.2 Expected Values
611(1)
A.4.3 Pairs of Discrete Random Variables
612(1)
A.4.4 Statistical Independence
613(1)
A.4.5 Expected Values of Functions of Two Variables
613(1)
A.4.6 Conditional Probability
614(1)
A.4.7 The Law of Total Probability and Bayes' Rule
615(1)
A.4.8 Vector Random Variables
616(1)
A.4.9 Expectations, Mean Vectors and Covariance Matrices
617(1)
A.4.10 Continuous Random Variables
618(2)
A.4.11 Distributions of Sums of Independent Random Variables
620(1)
A.4.12 Normal Distributions
621(2)
A.5 Gaussian Derivatives and Integrals
623(5)
A.5.1 Multivariate Normal Densities
624(2)
A.5.2 Bivariate Normal Densities
626(2)
A.6 Hypothesis Testing
628(2)
A.6.1 Chi-Squared Test
629(1)
A.7 Information Theory
630(3)
A.7.1 Entropy and Information
630(2)
A.7.2 Relative Entropy
632(1)
A.7.3 Mutual Information
632(1)
A.8 Computational Complexity
633(4)
Bibliography
635(2)
Index 637

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