| 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 |
図書
