Neural Networks and Learning Machines

ISBN-10: 0131471392

ISBN-13: 9780131471399

Authors: Simon Haykin

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Fluid and authoritative, this well-organized book represents the first comprehensive treatment of neural networks from an engineering perspective, providing extensive, state-of-the-art coverage that will expose readers to the myriad facets of neural networks and help them appreciate the technologys origin, capabilities, and potential applications.Examines all the important aspects of this emerging technolgy, covering the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementation. Integrates computer experiments throughout to demonstrate how neural networks are designed…

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Binding: Hardcover Publisher: Pearson Education Number of pages: 936 Dimensions: 7.30" wide x 9.50" long x 1.95" tall Weight: 3.608 lbs. Language: English



Preface


Introduction



What is a Neural Network?



The Human Brain



Models of a Neuron



Neural Networks Viewed As Directed Graphs



Feedback



Network Architectures



Knowledge Representation



Learning Processes



Learning Tasks



Concluding Remarks


Notes and References



Rosenblatt's Perceptron



Introduction



Perceptron



The Perceptron Convergence Theorem



Relation Between the Perceptron and Bayes Classifier for a Gaussian Environment



Computer Experiment: Pattern Classification



The Batch Perceptron Algorithm



Summary and Discussion


Notes and References


Problems



Model Building through Regression



Introduction



Linear Regression Model: Preliminary Considerations



Maximum a Posteriori Estimation of the Parameter Vector



Relationship Between Regularized Least-Squares Estimation and MAP Estimation



Computer Experiment: Pattern Classification



The Minimum-Description-Length Principle



Finite Sample-Size Considerations



The Instrumental-Variables Method



Summary and Discussion


Notes and References


Problems



The Least-Mean-Square Algorithm



Introduction



Filtering Structure of the LMS Algorithm



Unconstrained Optimization: a Review



The Wiener Filter



The Least-Mean-Square Algorithm



Markov Model Portraying the Deviation of the LMS Algorithm from the Wiener Filter



The Langevin Equation: Characterization of Brownian Motion



Kushner's Direct-Averaging Method



Statistical LMS Learning Theory for Small Learning-Rate Parameter



Computer Experiment I: Linear Prediction



Computer Experiment II: Pattern Classification



Virtues and Limitations of the LMS Algorithm



Learning-Rate Annealing Schedules



Summary and Discussion


Notes and References


Problems



Multilayer Perceptrons



Introduction



Some Preliminaries



Batch Learning and On-Line Learning



The Back-Propagation Algorithm



XOR Problem



Heuristics for Making the Back-Propagation Algorithm Perform Better



Computer Experiment: Pattern Classification



Back Propagation and Differentiation



The Hessian and Its Role in On-Line Learning



Optimal Annealing and Adaptive Control of the Learning Rate



Generalization



Approximations of Functions



Cross-Validation



Complexity Regularization and Network Pruning



Virtues and Limitations of Back-Propagation Learning



Supervised Learning Viewed as an Optimization Problem



Convolutional Networks



Nonlinear Filtering



Small-Scale Versus Large-Scale Learning Problems



Summary and Discussion


Notes and References


Problems



Kernel Methods and Radial-Basis Function Networks



Introduction



Cover's Theorem on the Separability of Patterns



The Interpolation Problem



Radial-Basis-Function Networks



K-Means Clustering



Recursive Least-Squares Estimation of the Weight Vector



Hybrid Learning Procedure for RBF Networks



Computer Experiment: Pattern Classification



Interpretations of the Gaussian Hidden Units



Kernel Regression and Its Relation to RBF Networks



Summary and Discussion


Notes and References


Problems



Support Vector Machines



Introduction



Optimal Hyperplane for Linearly Separable Patterns



Optimal Hyperplane for Nonseparable Patterns



The Support Vector Machine Viewed as a Kernel Machine



Design of Support Vector Machines



XOR Problem



Computer Experiment: Pattern Classification



Regression: Robustness Considerations



Optimal Solution of the Linear Regression Problem



The Representer Theorem and Related Issues



Summary and Discussion


Notes and References


Problems



Regularization Theory



Introduction



Hadamard's Conditions for Well-Posedness



Tikhonov's Regularization Theory



Regularization Networks



Generalized Radial-Basis-Function Networks



The Regularized Least-Squares Estimator: Revisited



Additional Notes of Interest on Regularization



Estimation of the Regularization Parameter



Semisupervised Learning



Manifold Regularization: Preliminary Considerations



Differentiable Manifolds



Generalized Regularization Theory



Spectral Graph Theory



Generalized Representer Theorem



Laplacian Regularized Least-Squares Algorithm



Experiments on Pattern Classification Using Semisupervised Learning



Summary and Discussion


Notes and References


Problems



Principal-Components Analysis



Introduction



Principles of Self-Organization



Self-Organized Feature Analysis



Principal-Components Analysis: Perturbation Theory



Hebbian-Based Maximum Eigenfilter



Hebbian-Based Principal-Components Analysis



Case Study: Image Coding



Kernel Principal-Components Analysis



Basic Issues Involved in the Coding of Natural Images



Kernel Hebbian Algorithm



Summary and Discussion


Notes and References


Problems



Self-Organizing Maps



Introduction



Two Basic Feature-Mapping Models



Self-Organizing Map



Properties of the Feature Map



Computer Experiments I: Disentangling Lattice Dynamics Using SOM



Contextual Maps



Hierarchical Vector Quantization



Kernel Self-Organizing Map



Computer Experiment II: Disentangling Lattice Dynamics Using Kernel SOM



Relationship Between Kernel SOM and Kullback-Leibler Divergence



Summary and Discussion


Notes and References


Problems



Information-Theoretic Learning Models



Introduction



Entropy



Maximum-Entropy Principle



Mutual Information



Kullback-Leibler Divergence



Copulas



Mutual Information as an Objective Function to be Optimized



Maximum Mutual Information Principle



Infomax and Redundancy Reduction



Spatially Coherent Features



Spatially Incoherent Features



Independent-Components Analysis



Sparse Coding of Natural Images and Comparison with ICA Coding



Natural-Gradient Learning for Independent-Components Analysis



Maximum-Likelihood Estimation for Independent-Components Analysis



Maximum-Entropy Learning for Blind Source Separation



Maximization of Negentropy for Independent-Components Analysis



Coherent Independent-Components Analysis



Rate Distortion Theory and Information Bottleneck



Optimal Manifold Representation of Data



Computer Experiment: Pattern Classification



Summary and Discussion


Notes and References


Problems



Stochastic Methods Rooted in Statistical Mechanics



Introduction



Statistical Mechanics



Markov Chains



Metropolis Algorithm



Simulated Annealing



Gibbs Sampling



Boltzmann Machine



Logistic Belief Nets



Deep Belief Nets



Deterministic Annealing



Analogy of Deterministic Annealing with Expectation-Maximization Algorithm



Summary and Discussion


Notes and References


Problems



Dynamic Programming



Introduction



Markov Decision Process



Bellman's Optimality Criterion



Policy Iteration



Value Iteration



Approximate Dynamic Programming: Direct Methods



Temporal-Difference Learning



Q-Learning



Approximate Dynamic Programming: Indirect Methods



Least-Squares Policy Evaluation



Approximate Policy Iteration



Summary and Discussion


Notes and References


Problems



Neurodynamics



Introduction



Dynamic Systems



Stability of Equilibrium States



Attractors



Neurodynamic Models



Manipulation of Attractors as a Recurrent Network Paradigm



Hopfield Model



The Cohen-Grossberg Theorem



Brain-State-In-A-Box Model



Strange Attractors and Chaos



Dynamic Reconstruction of a Chaotic Process



Summary and Discussion


Notes and References


Problems



Bayseian Filtering for State Estimation of Dynamic Systems



Introduction



State-Space Models



Kalman Filters



The Divergence-Phenomenon and Square-Root Filtering



The Extended Kalman Filter



The Bayesian Filter



Cubature Kalman Filter: Building on the Kalman Filter



Particle Filters



Computer Experiment: Comparative Evaluation of Extended Kalman and Particle Filters



Kalman Filtering in Modeling of Brain Functions



Summary and Discussion


Notes and References


Problems



Dynamically Driven Recurrent Networks



Introduction



Recurrent Network Architectures



Universal Approximation Theorem



Controllability and Observability



Computational Power of Recurrent Networks



Learning Algorithms



Back Propagation Through Time



Real-Time Recurrent Learning



Vanishing Gradients in Recurrent Networks



Supervised Training Framework for Recurrent Networks Using Nonlinear Sequential State Estimators



Computer Experiment: Dynamic Reconstruction of Mackay-Glass Attractor



Adaptivity Considerations



Case Study: Model Reference Applied to Neurocontrol



Summary and Discussion


Notes and References


Problems


Bibliography


Index

Neural Networks and Learning Machines

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