Neural network learning and expert systems

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Neural network learning and expert systems / Stephen I. Gallant

資料種別:: 図書
出版情報:: Cambridge, Mass. : MIT Press, c1993
形態:: xvi, 365 p. ; 24 cm
シリーズ名:: Bradford book <BA00245453>
著者名:: Gallant, Stephen I. <DA07315258>
ISBN:: 9780262071451 [0262071452]
書誌ID:: BA20017790

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注記:

Includes bibliographical references (p. [349]-359) and index

主題:

Expert systems (Computer science); Neural networks (Computer science)

言語:

英語

目次情報:

Foreword

Basics / I：

Introduction and Important Definitions / 1：

Why Connectionist Models? / 1.1：

The Grand Goals of Al and Its Current Impasse / 1.1.1：

The Computational Appeal of Neural Networks / 1.1.2：

The Structure of Connectionist Models / 1.2：

Network Properties / 1.2.1：

Cell Properties / 1.2.2：

Dynamic Properties / 1.2.3：

Learning Properties / 1.2.4：

Two Fundamental Models: Multilayer Perceptrons (MLP's) and Backpropagation Networks (BPN's) / 1.3：

Multilayer Perceptrons (MLP's) / 1.3.1：

Backpropagation Networks (BPN's) / 1.3.2：

Gradient Descent / 1.4：

The Algorithm / 1.4.1：

Practical Problems / 1.4.2：

Comments / 1.4.3：

Historic and Bibliographic Notes / 1.5：

Early Work / 1.5.1：

The Decline of the Perceptron / 1.5.2：

The Rise of Connectionist Research / 1.5.3：

Other Bibliographic Notes / 1.5.4：

Exercises / 1.6：

Programming Project / 1.7：

Representation Issues / 2：

Representing Boolean Functions / 2.1：

Equivalence of {+1, -1,0} and {1,0} Forms / 2.1.1：

Single-Cell Models / 2.1.2：

Nonseparable Functions / 2.1.3：

Representing Arbitrary Boolean Functions / 2.1.4：

Representing Boolean Functions Using Continuous Connectionist Models / 2.1.5：

Distributed Representations / 2.2：

Definition / 2.2.1：

Storage Efficiency and Resistance to Error / 2.2.2：

Superposition / 2.2.3：

Learning / 2.2.4：

Feature Spaces and ISA Relations / 2.3：

Feature Spaces / 2.3.1：

Concept-Function Unification / 2.3.2：

ISA Relations / 2.3.3：

Binding / 2.3.4：

Representing Real-Valued Functions / 2.4：

Approximating Real Numbers by Collections of Discrete Cells / 2.4.1：

Precision / 2.4.2：

Approximating Real Numbers by Collections of Continuous Cells / 2.4.3：

Example: Taxtime! / 2.5：

Programming Projects / 2.6：

Learning In Single-Layer Models / II：

Perceptron Learning and the Pocket Algorithm / 3：

Perceptron Learning for Separable Sets of Training Examples / 3.1：

Statement of the Problem / 3.1.1：

Computing the Bias / 3.1.2：

The Perceptron Learning Algorithm / 3.1.3：

Perceptron Convergence Theorem / 3.1.4：

The Perceptron Cycling Theorem / 3.1.5：

The Pocket Algorithm for Nonseparable Sets of Training Examples / 3.2：

Problem Statement / 3.2.1：

Perceptron Learning Is Poorly Behaved / 3.2.2：

The Pocket Algorithm / 3.2.3：

Ratchets / 3.2.4：

Examples / 3.2.5：

Noisy and Contradictory Sets of Training Examples / 3.2.6：

Rules / 3.2.7：

Implementation Considerations / 3.2.8：

Proof of the Pocket Convergence Theorem / 3.2.9：

Khachiyan's Linear Programming Algorithm / 3.3：

Winner-Take-All Groups or Linear Machines / 3.4：

Generalizes Single-Cell Models / 4.1：

Perceptron Learning for Winner-Take-All Groups / 4.2：

The Pocket Algorithm for Winner-Take-All Groups / 4.3：

Kessler's Construction, Perceptron Cycling, and the Pocket Algorithm Proof / 4.4：

Independent Training / 4.5：

Autoassociators and One-Shot Learning / 4.6：

Linear Autoassociators and the Outer-Product Training Rule / 5.1：

Anderson's BSB Model / 5.2：

Hopfieid's Model / 5.3：

Energy / 5.3.1：

The Traveling Salesman Problem / 5.4：

The Cohen-Grossberg Theorem / 5.5：

Kanerva's Model / 5.6：

Autoassociative Filtering for Feedforward Networks / 5.7：

Concluding Remarks / 5.8：

Mean Squared Error (MSE) Algorithms / 5.9：

Motivation / 6.1：

MSE Approximations / 6.2：

The Widrow-Hoff Rule or LMS Algorithm / 6.3：

Number of Training Examples Required / 6.3.1：

Adaline / 6.4：

Adaptive Noise Cancellation / 6.5：

Decision-Directed Learning / 6.6：

Unsupervised Learning / 6.7：

Introduction / 7.1：

No Teacher / 7.1.1：

Clustering Algorithms / 7.1.2：

k-Means Clustering / 7.2：

Topology-Preserving Maps / 7.2.1：

Example / 7.3.1：

Demonstrations / 7.3.4：

Dimensionality, Neighborhood Size, and Final Comments / 7.3.5：

Art1 / 7.4：

Important Aspects of the Algorithm / 7.4.1：

Art2 / 7.4.2：

Using Clustering Algorithms for Supervised Learning / 7.6：

Labeling Clusters / 7.6.1：

ARTMAP or Supervised ART / 7.6.2：

Learning In Multilayer Models / 7.7：

The Distributed Method and Radial Basis Functions / 8：

Rosenblatt's Approach / 8.1：

The Distributed Method / 8.2：

Cover's Formula / 8.2.1：

Robustness-Preserving Functions / 8.2.2：

Hepatobiliary Data / 8.3：

Artificial Data / 8.3.2：

How Many Cells? / 8.4：

Pruning Data / 8.4.1：

Leave-One-Out / 8.4.2：

Radial Basis Functions / 8.5：

A Variant: The Anchor Algorithm / 8.6：

Scaling, Multiple Outputs, and Parallelism / 8.7：

Scaling Properties / 8.7.1：

Multiple Outputs and Parallelism / 8.7.2：

A Computational Speedup for Learning / 8.7.3：

Computational Learning Theory and the BRD Algorithm / 8.7.4：

Introduction to Computational Learning Theory / 9.1：

PAC-Learning / 9.1.1：

Bounded Distributed Connectionist Networks / 9.1.2：

Probabilistic Bounded Distributed Concepts / 9.1.3：

A Learning Algorithm for Probabilistic Bounded Distributed Concepts / 9.2：

The BRD Theorem / 9.3：

Polynomial Learning / 9.3.1：

Noisy Data and Fallback Estimates / 9.4：

Vapnik-Chervonenkis Bounds / 9.4.1：

Hoeffding and Chernoff Bounds / 9.4.2：

Pocket Algorithm / 9.4.3：

Additional Training Examples / 9.4.4：

Bounds for Single-Layer Algorithms / 9.5：

Fitting Data by Limiting the Number of Iterations / 9.6：

Discussion / 9.7：

Exercise / 9.8：

Constructive Algorithms / 9.9：

The Tower and Pyramid Algorithms / 10.1：

The Tower Algorithm / 10.1.1：

Proof of Convergence / 10.1.2：

A Computational Speedup / 10.1.4：

The Pyramid Algorithm / 10.1.5：

The Cascade-Correlation Algorithm / 10.2：

The Tiling Algorithm / 10.3：

The Upstart Algorithm / 10.4：

Other Constructive Algorithms and Pruning / 10.5：

Easy Learning Problems / 10.6：

Decomposition / 10.6.1：

Expandable Network Problems / 10.6.2：

Limits of Easy Learning / 10.6.3：

Backpropagation / 10.7：

The Backpropagation Algorithm / 11.1：

Statement of the Algorithm / 11.1.1：

A Numerical Example / 11.1.2：

Derivation / 11.2：

Practical Considerations / 11.3：

Determination of Correct Outputs / 11.3.1：

Initial Weights / 11.3.2：

Choice of r / 11.3.3：

Momentum / 11.3.4：

Network Topology / 11.3.5：

Local Minima / 11.3.6：

Activations in [0,1] versus [-1, 1] / 11.3.7：

Update after Every Training Example / 11.3.8：

Other Squashing Functions / 11.3.9：

NP-Completeness / 11.4：

Overuse / 11.5：

Interesting Intermediate Cells / 11.5.2：

Continuous Outputs / 11.5.3：

Probability Outputs / 11.5.4：

Using Backpropagation to Train Multilayer Perceptrons / 11.5.5：

Backpropagation: Variations and Applications / 11.6：

NETtalk / 12.1：

Input and Output Representations / 12.1.1：

Experiments / 12.1.2：

Backpropagation through Time / 12.1.3：

Handwritten Character Recognition / 12.3：

Neocognitron Architecture / 12.3.1：

The Network / 12.3.2：

Robot Manipulator with Excess Degrees of Freedom / 12.3.3：

The Problem / 12.4.1：

Training the Inverse Network / 12.4.2：

Plan Units / 12.4.3：

Simulated Annealing and Boltzmann Machines / 12.4.4：

Simulated Annealing / 13.1：

Boltzmann Machines / 13.2：

The Boltzmann Model / 13.2.1：

Boltzmann Learning / 13.2.2：

The Boltzmann Algorithm and Noise Clamping / 13.2.3：

Example: The 4-2-4 Encoder Problem / 13.2.4：

Remarks / 13.3：

Neural Network Expert Systems / 13.4：

Expert Systems and Neural Networks / 14：

Expert Systems / 14.1：

What Is an Expert System? / 14.1.1：

Why Expert Systems? / 14.1.2：

Historically Important Expert Systems / 14.1.3：

Critique of Conventional Expert Systems / 14.1.4：

Neural Network Decision Systems / 14.2：

Example: Diagnosis of Acute Coronary Occlusion / 14.2.1：

Example: Autonomous Navigation / 14.2.2：

Other Examples / 14.2.3：

Decision Systems versus Expert Systems / 14.2.4：

MACIE, and an Example Problem / 14.3：

Diagnosis and Treatment of Acute Sarcophagal Disease / 14.3.1：

Network Generation / 14.3.2：

Sample Run of Macie / 14.3.3：

Real-Valued Variables and Winner-Take-All Groups / 14.3.4：

Not-Yet-Known versus Unavailable Variables / 14.3.5：

Applicability of Neural Network Expert Systems / 14.4：

Details of the MACIE System / 14.5：

Inferencing and Forward Chaining / 15.1：

Discrete Multilayer Perceptron Models / 15.1.1：

Continuous Variables / 15.1.2：

Winner-Take-All Groups / 15.1.3：

Using Prior Probabilities for More Aggressive Inferencing / 15.1.4：

Confidence Estimation / 15.2：

A Confidence Heuristic Prior to Inference / 15.2.1：

Confidence in Inferences / 15.2.2：

Information Acquisition and Backward Chaining / 15.3：

Concluding Comment / 15.4：

Noise, Redundancy, Fault Detection, and Bayesian Decision Theory / 15.5：

The High Tech Lemonade Corporation's Problem / 16.1：

The Deep Model and the Noise Model / 16.2：

Generating the Expert System / 16.3：

Probabilistic Analysis / 16.4：

Noisy Single-Pattern Boolean Fault Detection Problems / 16.5：

Convergence Theorem / 16.6：

Extracting Rules from networks / 16.7：

Why Rules? / 17.1：

What Kind of Rules? / 17.2：

Criteria / 17.2.1：

Inference Justifications versus Rule Sets / 17.2.2：

Which Variables in Conditions / 17.2.3：

Inference Justifications / 17.3：

MACIE's Algorithm / 17.3.1：

The Removal Algorithm / 17.3.2：

Key Factor Justifications / 17.3.3：

Justifications for Continuous Models / 17.3.4：

Rule Sets / 17.4：

Limiting the Number of Conditions / 17.4.1：

Approximating Rules / 17.4.2：

Conventional + Neural Network Expert Systems / 17.5：

Debugging an Expert System Knowledge Base / 17.5.1：

The Short-Rule Debugging Cycle / 17.5.2：

Appendix Representation Comparisons / 17.6：

DNF Expressions / A.1 DNF Expressions and Polynomial Representability：

Polynomial Representability / A.1.2：

Space Comparison of MLP and DNF Representations / A.1.3：

Speed Comparison of MLP and DNF Representations / A.1.4：

MLP versus DNF Representations / A.1.5：

Decision Trees / A.2：

Representing Decision Trees by MLP's / A.2.1：

Speed Comparison / A.2.2：

Decision Trees versus MLP's / A.2.3：

p-lDiagrams / A.3：

Symmetric Functions and Depth Complexity / A.4：

Bibliography / A.5：

Index

Foreword

Basics / I：

Introduction and Important Definitions / 1：

Why Connectionist Models? / 1.1：

The Grand Goals of Al and Its Current Impasse / 1.1.1：

The Computational Appeal of Neural Networks / 1.1.2：

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