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Pattern Classification

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ISBN-10: 0471056693

ISBN-13: 9780471056690

Edition: 2nd 2001 (Revised)

Authors: Richard O. Duda, Peter E. Hart, David G. Stork

List price: $196.95
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Description:

This edition has been completely revised, enlarged and formatted in two colour. It is a systematic account of the major topics in pattern recognition, based on the fundamental principles. It includes extensive examples, exercises and a solutions manual.
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Book details

List price: $196.95
Edition: 2nd
Copyright year: 2001
Publisher: John Wiley & Sons, Incorporated
Publication date: 11/9/2000
Binding: Hardcover
Pages: 688
Size: 6.80" wide x 9.90" long x 1.20" tall
Weight: 3.014
Language: English

Preface
Introduction
Machine Perception
An Example
Related Fields
Pattern Recognition Systems
Sensing
Segmentation and Grouping
Feature Extraction
Classification
Post Processing
The Design Cycle
Data Collection
Feature Choice
Model Choice
Training
Evaluation
Computational Complexity
Learning and Adaptation
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Conclusion
Summary by Chapters
Bibliographical and Historical Remarks
Bibliography
Bayesian Decision Theory
Introduction
Bayesian Decision Theory--Continuous Features
Two-Category Classification
Minimum-Error-Rate Classification
Minimax Criterion
Neyman-Pearson Criterion
Classifiers, Discriminant Functions, and Decision Surfaces
The Multicategory Case
The Two-Category Case
The Normal Density
Univariate Density
Multivariate Density
Discriminant Functions for the Normal Density
Case 1: [Sigma subscript i] = [sigma superscript 2]I
Case 2: [Sigma ubscript i] = [Sigma]
Case 3: [Sigma subscript i] = arbitrary
Decision Regions for Two-Dimensional Gaussian Data
Error Probabilities and Integrals
Error Bounds for Normal Densities
Chernoff Bound
Bhattacharyya Bound
Error Bounds for Gaussian Distributions
Signal Detection Theory and Operating Characteristics
Bayes Decision Theory--Discrete Features
Independent Binary Features
Bayesian Decisions for Three-Dimensional Binary Data
Missing and Noisy Features
Missing Features
Noisy Features
Bayesian Belief Networks
Belief Network for Fish
Compound Bayesian Decision Theory and Context
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Maximum-Likelihood and Bayesian Parameter Estimation
Introduction
Maximum-Likelihood Estimation
The General Principle
The Gaussian Case: Unknown [mu]
The Gaussian Case: Unknown [mu] and [Sigma]
Bias
Bayesian Estimation
The Class-Conditional Densities
The Parameter Distribution
Bayesian Parameter Estimation: Gaussian Case
The Univariate Case: p([mu]D)
The Univariate Case: p(xD)
The Multivariate Case
Bayesian Parameter Estimation: General Theory
Recursive Bayes Learning
When Do Maximum-Likelihood and Bayes Methods Differ?
Noninformative Priors and Invariance
Gibbs Algorithm
Sufficient Statistics
Sufficient Statistics and the Exponential Family
Problems of Dimensionality
Accuracy, Dimension, and Training Sample Size
Computational Complexity
Overfitting
Component Analysis and Discriminants
Principal Component Analysis (PCA)
Fisher Linear Discriminant
Multiple Discriminant Analysis
Expectation-Maximization (EM)
Expectation-Maximization for a 2D Normal Model
Hidden Markov Models
First-Order Markov Models
First-Order Hidden Markov Models
Hidden Markov Model Computation
Evaluation
Hidden Markov Model
Decoding
HMM Decoding
Learning
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Nonparametric Techniques
Introduction
Density Estimation
Parzen Windows
Convergence of the Mean
Convergence of the Variance
Illustrations
Classification Example
Probabilistic Neural Networks (PNNs)
Choosing the Window Function
k[subscript n]-Nearest-Neighbor Estimation
k[subscript n]-Nearest-Neighbor and Parzen-Window Estimation
Estimation of A Posteriori Probabilities
The Nearest-Neighbor Rule
Convergence of the Nearest Neighbor
Error Rate for the Nearest-Neighbor Rule
Error Bounds
The k-Nearest-Neighbor Rule
Computational Complexity of the k-Nearest-Neighbor Rule
Metrics and Nearest-Neighbor Classification
Properties of Metrics
Tangent Distance
Fuzzy Classification
Reduced Coulomb Energy Networks
Approximations by Series Expansions
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Linear Discriminant Functions
Introduction
Linear Discriminant Functions and Decision Surfaces
The Two-Category Case
The Multicategory Case
Generalized Linear Discriminant Functions
The Two-Category Linearly Separable Case
Geometry and Terminology
Gradient Descent Procedures
Minimizing the Perceptron Criterion Function
The Perceptron Criterion Function
Convergence Proof for Single-Sample Correction
Some Direct Generalizations
Relaxation Procedures
The Descent Algorithm
Convergence Proof
Nonseparable Behavior
Minimum Squared-Error Procedures
Minimum Squared-Error and the Pseudoinverse
Constructing a Linear Classifier by Matrix Pseudoinverse
Relation to Fisher's Linear Discriminant
Asymptotic Approximation to an Optimal Discriminant
The Widrow-Hoff or LMS Procedure
Stochastic Approximation Methods
The Ho-Kashyap Procedures
The Descent Procedure
Convergence Proof
Nonseparable Behavior
Some Related Procedures
Linear Programming Algorithms
Linear Programming
The Linearly Separable Case
Minimizing the Perceptron Criterion Function
Support Vector Machines
SVM Training
SVM for the XOR Problem
Multicategory Generalizations
Kesler's Construction
Convergence of the Fixed-Increment Rule
Generalizations for MSE Procedures
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Multilayer Neural Networks
Introduction
Feedforward Operation and Classification
General Feedforward Operation
Expressive Power of Multilayer Networks
Backpropagation Algorithm
Network Learning
Training Protocols
Learning Curves
Error Surfaces
Some Small Networks
The Exclusive-OR (XOR)
Larger Networks
How Important Are Multiple Minima?
Backpropagation as Feature Mapping
Representations at the Hidden Layer--Weights
Backpropagation, Bayes Theory and Probability
Bayes Discriminants and Neural Networks
Outputs as Probabilities
Related Statistical Techniques
Practical Techniques for Improving Backpropagation
Activation Function
Parameters for the Sigmoid
Scaling Input
Target Values
Training with Noise
Manufacturing Data
Number of Hidden Units
Initializing Weights
Learning Rates
Momentum
Weight Decay
Hints
On-Line, Stochastic or Batch Training?
Stopped Training
Number of Hidden Layers
Criterion Function
Second-Order Methods
Hessian Matrix
Newton's Method
Quickprop
Conjugate Gradient Descent
Conjugate Gradient Descent
Additional Networks and Training Methods
Radial Basis Function Networks (RBFs)
Special Bases
Matched Filters
Convolutional Networks
Recurrent Networks
Cascade-Correlation
Regularization, Complexity Adjustment and Pruning
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Stochastic Methods
Introduction
Stochastic Search
Simulated Annealing
The Boltzmann Factor
Deterministic Simulated Annealing
Boltzmann Learning
Stochastic Boltzmann Learning of Visible States
Missing Features and Category Constraints
Deterministic Boltzmann Learning
Initialization and Setting Parameters
Boltzmann Networks and Graphical Models
Other Graphical Models
Evolutionary Methods
Genetic Algorithms
Further Heuristics
Why Do They Work?
Genetic Programming
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Nonmetric Methods
Introduction
Decision Trees
Cart
Number of Splits
Query Selection and Node Impurity
When to Stop Splitting
Pruning
Assignment of Leaf Node Labels
A Simple Tree
Computational Complexity
Feature Choice
Multivariate Decision Trees
Priors and Costs
Missing Attributes
Surrogate Splits and Missing Attributes
Other Tree Methods
ID3
C4.5
Which Tree Classifier Is Best?
Recognition with Strings
String Matching
Edit Distance
Computational Complexity
String Matching with Errors
String Matching with the "Don't-Care" Symbol
Grammatical Methods
Grammars
Types of String Grammars
A Grammar for Pronouncing Numbers
Recognition Using Grammars
Grammatical Inference
Grammatical Inference
Rule-Based Methods
Learning Rules
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Algorithm-Independent Machine Learning
Introduction
Lack of Inherent Superiority of Any Classifier
No Free Lunch Theorem
No Free Lunch for Binary Data
Ugly Duckling Theorem
Minimum Description Length (MDL)
Minimum Description Length Principle
Overfitting Avoidance and Occam's Razor
Bias and Variance
Bias and Variance for Regression
Bias and Variance for Classification
Resampling for Estimating Statistics
Jackknife
Jackknife Estimate of Bias and Variance of the Mode
Bootstrap
Resampling for Classifier Design
Bagging
Boosting
Learning with Queries
Arcing, Learning with Queries, Bias and Variance
Estimating and Comparing Classifiers
Parametric Models
Cross-Validation
Jackknife and Bootstrap Estimation of Classification Accuracy
Maximum-Likelihood Model Comparison
Bayesian Model Comparison
The Problem-Average Error Rate
Predicting Final Performance from Learning Curves
The Capacity of a Separating Plane
Combining Classifiers
Component Classifiers with Discriminant Functions
Component Classifiers without Discriminant Functions
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Unsupervised Learning and Clustering
Introduction
Mixture Densities and Identifiability
Maximum-Likelihood Estimates
Application to Normal Mixtures
Case 1: Unknown Mean Vectors
Case 2: All Parameters Unknown
k-Means Clustering
Fuzzy k-Means Clustering
Unsupervised Bayesian Learning
The Bayes Classifier
Learning the Parameter Vector
Unsupervised Learning of Gaussian Data
Decision-Directed Approximation
Data Description and Clustering
Similarity Measures
Criterion Functions for Clustering
The Sum-of-Squared-Error Criterion
Related Minimum Variance Criteria
Scatter Criteria
Clustering Criteria
Iterative Optimization
Hierarchical Clustering
Definitions
Agglomerative Hierarchical Clustering
Stepwise-Optimal Hierarchical Clustering
Hierarchical Clustering and Induced Metrics
The Problem of Validity
On-line clustering
Unknown Number of Clusters
Adaptive Resonance
Learning with a Critic
Graph-Theoretic Methods
Component Analysis
Principal Component Analysis (PCA)
Nonlinear Component Analysis (NLCA)
Independent Component Analysis (ICA)
Low-Dimensional Representations and Multidimensional Scaling (MDS)
Self-Organizing Feature Maps
Clustering and Dimensionality Reduction
Summary
Bibliographical and Historical Remarks
Problems
Computer exercises
Bibliography
Mathematical Foundations
Notation
Linear Algebra
Notation and Preliminaries
Inner Product
Outer Product
Derivatives of Matrices
Determinant and Trace
Matrix Inversion
Eigenvectors and Eigenvalues
Lagrange Optimization
Probability Theory
Discrete Random Variables
Expected Values
Pairs of Discrete Random Variables
Statistical Independence
Expected Values of Functions of Two Variables
Conditional Probability
The Law of Total Probability and Bayes' Rule
Vector Random Variables
Expectations, Mean Vectors and Covariance Matrices
Continuous Random Variables
Distributions of Sums of Independent Random Variables
Normal Distributions
Gaussian Derivatives and Integrals
Multivariate Normal Densities
Bivariate Normal Densities
Hypothesis Testing
Chi-Squared Test
Information Theory
Entropy and Information
Relative Entropy
Mutual Information
Computational Complexity
Bibliography
Index
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