Neural networks
Subtitle: a comprehensive foundation
By: Simon Haykin.
Edition: 2nd ed.
Publisher: Prentice Hall
Published: 1999
Place of publication: Upper Saddle River, N.J
Language: eng
Number of pages: 842
Subjects: Neural networks (Computer science)
Notes: Includes bibliographical references (p. 796-836) and index.
Table of contents: title: Preface
pagenum: xii; title: Acknowledgments
pagenum: xv; title: Abbreviations and Symbols
pagenum: xvii; label: 1
title: Introduction
pagenum: 1; label: 1.1
title: What Is a Neural Network?
pagenum: 1; label: 1.2
title: Human Brain
pagenum: 6; label: 1.3
title: Models of a Neuron
pagenum: 10; label: 1.4
title: Neural Networks Viewed as Directed Graphs
pagenum: 15; label: 1.5
title: Feedback
pagenum: 18; label: 1.6
title: Network Architectures
pagenum: 21; label: 1.7
title: Knowledge Representation
pagenum: 23; label: 1.8
title: Artificial Intelligence and Neural Networks
pagenum: 34; label: 1.9
title: Historical Notes
pagenum: 38; title: Notes and References
pagenum: 45; title: Problems
pagenum: 45; label: 2
title: Learning Processes
pagenum: 50; label: 2.1
title: Introduction
pagenum: 50; label: 2.2
title: Error-Correction Learning
pagenum: 51; label: 2.3
title: Memory-Based Learning
pagenum: 53; label: 2.4
title: Hebbian Learning
pagenum: 55; label: 2.5
title: Competitive Learning
pagenum: 58; label: 2.6
title: Boltzmann Learning
pagenum: 60; label: 2.7
title: Credit Assignment Problem
pagenum: 62; label: 2.8
title: Learning with a Teacher
pagenum: 63; label: 2.9
title: Learning without a Teacher
pagenum: 64; label: 2.10
title: Learning Tasks
pagenum: 66; label: 2.11
title: Memory
pagenum: 75; label: 2.12
title: Adaptation
pagenum: 83; label: 2.13
title: Statistical Nature of the Learning Process
pagenum: 84; label: 2.14
title: Statistical Learning Theory
pagenum: 89; label: 2.15
title: Probably Approximately Correct Model of Learning
pagenum: 102; label: 2.16
title: Summary and Discussion
pagenum: 105; title: Notes and References
pagenum: 106; title: Problems
pagenum: 111; label: 3
title: Single Layer Perceptrons
pagenum: 117; label: 3.1
title: Introduction
pagenum: 117; label: 3.2
title: Adaptive Filtering Problem
pagenum: 118; label: 3.3
title: Unconstrained Optimization Techniques
pagenum: 121; label: 3.4
title: Linear Least-Squares Filters
pagenum: 126; label: 3.5
title: Least-Mean-Square Algorithm
pagenum: 128; label: 3.6
title: Learning Curves
pagenum: 133; label: 3.7
title: Learning Rate Annealing Techniques
pagenum: 134; label: 3.8
title: Perceptron
pagenum: 135; label: 3.9
title: Perceptron Convergence Theorem
pagenum: 137; label: 3.10
title: Relation Between the Perceptron and Bayes Classifier for a Gaussian Environment
pagenum: 143; label: 3.11
title: Summary and Discussion
pagenum: 148; title: Notes and References
pagenum: 150; title: Problems
pagenum: 151; label: 4
title: Multilayer Perceptrons
pagenum: 156; label: 4.1
title: Introduction
pagenum: 156; label: 4.2
title: Some Preliminaries
pagenum: 159; label: 4.3
title: Back-Propagation Algorithm
pagenum: 161; label: 4.4
title: Summary of the Back-Propagation Algorithm
pagenum: 173; label: 4.5
title: XOR Problem
pagenum: 175; label: 4.6
title: Heuristics for Making the Back-Propagation Algorithm Perform Better
pagenum: 178; label: 4.7
title: Output Representation and Decision Rule
pagenum: 184; label: 4.8
title: Computer Experiment
pagenum: 187; label: 4.9
title: Feature Detection
pagenum: 199; label: 4.10
title: Back-Propagation and Differentiation
pagenum: 202; label: 4.11
title: Hessian Matrix
pagenum: 204; label: 4.12
title: Generalization
pagenum: 205; label: 4.13
title: Approximations of Functions
pagenum: 208; label: 4.14
title: Cross-Validation
pagenum: 213; label: 4.15
title: Network Pruning Techniques
pagenum: 218; label: 4.16
title: Virtues and Limitations of Back-Propagation Learning
pagenum: 226; label: 4.17
title: Accelerated Convergence of Back-Propagation Learning
pagenum: 233; label: 4.18
title: Supervised Learning Viewed as an Optimization Problem
pagenum: 234; label: 4.19
title: Convolutional Networks
pagenum: 245; label: 4.20
title: Summary and Discussion
pagenum: 247; title: Notes and References
pagenum: 248; title: Problems
pagenum: 252; label: 5
title: Radial-Basis Function Networks
pagenum: 256; label: 5.1
title: Introduction
pagenum: 256; label: 5.2
title: Cover's Theorem on the Separability of Patterns
pagenum: 257; label: 5.3
title: Interpolation Problem
pagenum: 262; label: 5.4
title: Supervised Learning as an Ill-Posed Hypersurface Reconstruction Problem
pagenum: 265; label: 5.5
title: Regularization Theory
pagenum: 267; label: 5.6
title: Regularization Networks
pagenum: 277; label: 5.7
title: Generalized Radial-Basis Function Networks
pagenum: 278; label: 5.8
title: XOR Problem (Revisited)
pagenum: 282; label: 5.9
title: Estimation of the Regularization Parameter
pagenum: 284; label: 5.10
title: Approximation Properties of RBF Networks
pagenum: 290; label: 5.11
title: Comparison of RBF Networks and Multilayer Perceptrons
pagenum: 293; label: 5.12
title: Kernel Regression and Its Relation to RBF Networks
pagenum: 294; label: 5.13
title: Learning Strategies
pagenum: 298; label: 5.14
title: Computer Experiment
pagenum: 305; label: 5.15
title: Summary and Discussion
pagenum: 308; title: Notes and References
pagenum: 308; title: Problems
pagenum: 312; label: 6
title: Support Vector Machines
pagenum: 318; label: 6.1
title: Introduction
pagenum: 318; label: 6.2
title: Optimal Hyperplane for Linearly Separable Patterns
pagenum: 319; label: 6.3
title: Optimal Hyperplane for Nonseparable Patterns
pagenum: 326; label: 6.4
title: How to Build a Support Vector Machine for Pattern Recognition
pagenum: 329; label: 6.5
title: Example: XOR Problem (Revisited)
pagenum: 335; label: 6.6
title: Computer Experiment
pagenum: 337; label: 6.7
title: E-Insensitive Loss Function
pagenum: 339; label: 6.8
title: Support Vector Machines for Nonlinear Regression
pagenum: 340; label: 6.9
title: Summary and Discussion
pagenum: 343; title: Notes and References
pagenum: 347; title: Problems
pagenum: 348; label: 7
title: Committee Machines
pagenum: 351; label: 7.1
title: Introduction
pagenum: 351; label: 7.2
title: Ensemble Averaging
pagenum: 353; label: 7.3
title: Computer Experiment I
pagenum: 355; label: 7.4
title: Boosting
pagenum: 357; label: 7.5
title: Computer Experiment II
pagenum: 364; label: 7.6
title: Associative Gaussian Mixture Model
pagenum: 366; label: 7.7
title: Hierarchical Mixture of Experts Model
pagenum: 372; label: 7.8
title: Model Selection Using a Standard Decision Tree
pagenum: 374; label: 7.9
title: A Priori and a Posteriori Probabilities
pagenum: 377; label: 7.10
title: Maximum Likelihood Estimation
pagenum: 378; label: 7.11
title: Learning Strategies for the HME Model
pagenum: 380; label: 7.12
title: EM Algorithm
pagenum: 382; label: 7.13
title: Application of the EM Algorithm to the HME Model
pagenum: 383; label: 7.14
title: Summary and Discussion
pagenum: 386; title: Notes and References
pagenum: 387; title: Problems
pagenum: 389; label: 8
title: Principal Components Analysis
pagenum: 392; label: 8.1
title: Introduction
pagenum: 392; label: 8.2
title: Some Intuitive Principles of Self-Organization
pagenum: 393; label: 8.3
title: Principal Components Analysis
pagenum: 396; label: 8.4
title: Hebbian-Based Maximum Eigenfilter
pagenum: 404; label: 8.5
title: Hebbian-Based Principal Components Analysis
pagenum: 413; label: 8.6
title: Computer Experiment: Image Coding
pagenum: 419; label: 8.7
title: Adaptive Principal Components Analysis Using Lateral Inhibition
pagenum: 422; label: 8.8
title: Two Classes of PCA Algorithms
pagenum: 430; label: 8.9
title: Batch and Adaptive Methods of Computation
pagenum: 430; label: 8.10
title: Kernel-Based Principal Components Analysis
pagenum: 432; label: 8.11
title: Summary and Discussion
pagenum: 437; title: Notes and References
pagenum: 439; title: Problems
pagenum: 440; label: 9
title: Self-Organizing Maps
pagenum: 443; label: 9.1
title: Introduction
pagenum: 443; label: 9.2
title: Two Basic Feature-Mapping Models
pagenum: 444; label: 9.3
title: Self-Organizing Map
pagenum: 446; label: 9.4
title: Summary of the SOM Algorithm
pagenum: 453; label: 9.5
title: Properties of the Feature Map
pagenum: 454; label: 9.6
title: Computer Simulations
pagenum: 461; label: 9.7
title: Learning Vector Quantization
pagenum: 466; label: 9.8
title: Computer Experiment: Adaptive Pattern Classification
pagenum: 468; label: 9.9
title: Hierarchical Vector Quantization
pagenum: 470; label: 9.10
title: Contextual Maps
pagenum: 474; label: 9.11
title: Summary and Discussion
pagenum: 476; title: Notes and References
pagenum: 477; title: Problems
pagenum: 479; label: 10
title: Information-Theoretic Models
pagenum: 484; label: 10.1
title: Introduction
pagenum: 484; label: 10.2
title: Entropy
pagenum: 485; label: 10.3
title: Maximum Entropy Principle
pagenum: 490; label: 10.4
title: Mutual Information
pagenum: 492; label: 10.5
title: Kullback-Leibler Divergence
pagenum: 495; label: 10.6
title: Mutual Information as an Objective Function to Be Optimized
pagenum: 498; label: 10.7
title: Maximum Mutual Information Principle
pagenum: 499; label: 10.8
title: Infomax and Redundancy Reduction
pagenum: 503; label: 10.9
title: Spatially Coherent Features
pagenum: 506; label: 10.10
title: Spatially Incoherent Features
pagenum: 508; label: 10.11
title: Independent Components Analysis
pagenum: 510; label: 10.12
title: Computer Experiment
pagenum: 523; label: 10.13
title: Maximum Likelihood Estimation
pagenum: 525; label: 10.14
title: Maximum Entropy Method
pagenum: 529; label: 10.15
title: Summary and Discussion
pagenum: 533; title: Notes and References
pagenum: 535; title: Problems
pagenum: 541; label: 11
title: Stochastic Machines and Their Approximates Rooted in Statistical Mechanics
pagenum: 545; label: 11.1
title: Introduction
pagenum: 545; label: 11.2
title: Statistical Mechanics
pagenum: 546; label: 11.3
title: Markov Chains
pagenum: 548; label: 11.4
title: Metropolis Algorithm
pagenum: 556; label: 11.5
title: Simulated Annealing
pagenum: 558; label: 11.6
title: Gibbs Sampling
pagenum: 561; label: 11.7
title: Boltzmann Machine
pagenum: 562; label: 11.8
title: Sigmoid Belief Networks
pagenum: 569; label: 11.9
title: Helmholtz Machine
pagenum: 574; label: 11.10
title: Mean-Field Theory
pagenum: 576; label: 11.11
title: Deterministic Boltzmann Machine
pagenum: 578; label: 11.12
title: Deterministic Sigmoid Belief Networks
pagenum: 579; label: 11.13
title: Deterministic Annealing
pagenum: 586; label: 11.14
title: Summary and Discussion
pagenum: 592; title: Notes and References
pagenum: 594; title: Problems
pagenum: 597; label: 12
title: Neurodynamic Programming
pagenum: 603; label: 12.1
title: Introduction
pagenum: 603; label: 12.2
title: Markovian Decision Processes
pagenum: 604; label: 12.3
title: Bellman's Optimality Criterion
pagenum: 607; label: 12.4
title: Policy Iteration
pagenum: 610; label: 12.5
title: Value Iteration
pagenum: 612; label: 12.6
title: Neurodynamic Programming
pagenum: 617; label: 12.7
title: Approximate Policy Iteration
pagenum: 618; label: 12.8
title: Q-Learning
pagenum: 622; label: 12.9
title: Computer Experiment
pagenum: 627; label: 12.10
title: Summary and Discussion
pagenum: 629; title: Notes and References
pagenum: 631; title: Problems
pagenum: 632; label: 13
title: Temporal Processing Using Feedforward Networks
pagenum: 635; label: 13.1
title: Introduction
pagenum: 635; label: 13.2
title: Short-Term Memory Structures
pagenum: 636; label: 13.3
title: Network Architectures for Temporal Processing
pagenum: 640; label: 13.4
title: Focused Time Lagged Feedforward Networks
pagenum: 643; label: 13.5
title: Computer Experiment
pagenum: 645; label: 13.6
title: Universal Myopic Mapping Theorem
pagenum: 646; label: 13.7
title: Spatio-Temporal Models of a Neuron
pagenum: 648; label: 13.8
title: Distributed Time Lagged Feedforward Networks
pagenum: 651; label: 13.9
title: Temporal Back-Propagation Algorithm
pagenum: 652; label: 13.10
title: Summary and Discussion
pagenum: 659; title: Notes and References
pagenum: 660; title: Problems
pagenum: 660; label: 14
title: Neurodynamics
pagenum: 664; label: 14.1
title: Introduction
pagenum: 664; label: 14.2
title: Dynamical Systems
pagenum: 666; label: 14.3
title: Stability of Equilibrium States
pagenum: 669; label: 14.4
title: Attractors
pagenum: 674; label: 14.5
title: Neurodynamical Models
pagenum: 676; label: 14.6
title: Manipulation of Attractors as a Recurrent Network Paradigm
pagenum: 680; label: 14.7
title: Hopfield Models
pagenum: 680; label: 14.8
title: Computer Experiment I
pagenum: 696; label: 14.9
title: Cohen-Grossberg Theorem
pagenum: 701; label: 14.10
title: Brain-State-in-a-Box Model
pagenum: 703; label: 14.11
title: Computer Experiment II
pagenum: 709; label: 14.12
title: Strange Attractors and Chaos
pagenum: 709; label: 14.13
title: Dynamic Reconstruction of a Chaotic Process
pagenum: 714; label: 14.14
title: Computer Experiment III
pagenum: 718; label: 14.15
title: Summary and Discussion
pagenum: 722; title: Notes and References
pagenum: 725; title: Problems
pagenum: 727; label: 15
title: Dynamically Driven Recurrent Networks
pagenum: 732; label: 15.1
title: Introduction
pagenum: 732; label: 15.2
title: Recurrent Network Architectures
pagenum: 733; label: 15.3
title: State-Space Model
pagenum: 739; label: 15.4
title: Nonlinear Autoregressive with Exogenous Inputs Model
pagenum: 746; label: 15.5
title: Computational Power of Recurrent Networks
pagenum: 747; label: 15.6
title: Learning Algorithms
pagenum: 750; label: 15.7
title: Back-Propagation Through Time
pagenum: 751; label: 15.8
title: Real-Time Recurrent Learning
pagenum: 756; label: 15.9
title: Kalman Filters
pagenum: 762; label: 15.10
title: Decoupled Extended Kalman Filters
pagenum: 765; label: 15.11
title: Computer Experiment
pagenum: 770; label: 15.12
title: Vanishing Gradients in Recurrent Networks
pagenum: 773; label: 15.13
title: System Identification
pagenum: 776; label: 15.14
title: Model-Reference Adaptive Control
pagenum: 780; label: 15.15
title: Summary and Discussion
pagenum: 782; title: Notes and References
pagenum: 783; title: Problems
pagenum: 785; title: Epilogue
pagenum: 790; title: Bibliography
pagenum: 796; title: Index
pagenum: 837