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1.

図書

図書
P.J.G. Lisboa, M.J. Taylor
出版情報: New York : Ellis Horwood, 1993  307 p. ; 25 cm
シリーズ名: Ellis Horwood workshop series
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2.

図書

図書
Albert Nigrin
出版情報: Cambridge, Mass. : MIT Press, c1993  xvii, 413 p. ; 24 cm
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3.

図書

図書
edited by Daniel Gardner
出版情報: Cambridge, Mass. : MIT Press, c1993  xii, 227 p. ; 26 cm
シリーズ名: Computational neuroscience
Bradford book
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4.

図書

図書
Margaret Euphrasia Sereno
出版情報: Cambridge, Mass. : MIT Press, c1993  vi, 181 p. ; 24 cm
シリーズ名: Neural network modeling and connectionism
Bradford book
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5.

図書

図書
A. Cichocki, R. Unbehauen
出版情報: New York : J. Wiley, 1993  xvii, 526 p. ; 24 cm
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目次情報: 続きを見る
Mathematical Preliminaries of Neurocomputing
Architectures and Electronic Implementation of Neural Network Models
Unconstrained Optimization and Learning Algorithms
Neural Networks for Linear, Quadratic Programming and Linear Complementarity Problems
A Neural Network Approach to the On-Line Solution of a System of Linear Algebraic Equations and Related Problems
Neural Networks for Matrix Algebra Problems
Neural Networks for Continuous, Nonlinear, Constrained Optimization Problems
Neural Networks for Estimation, Identification and Prediction
Neural Networks for Discrete and Combinatorial Optimization Problems
Appendices
Subject Index
Mathematical Preliminaries of Neurocomputing
Architectures and Electronic Implementation of Neural Network Models
Unconstrained Optimization and Learning Algorithms
6.

図書

図書
Stephen I. Gallant
出版情報: Cambridge, Mass. : MIT Press, c1993  xvi, 365 p. ; 24 cm
シリーズ名: Bradford book
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目次情報: 続きを見る
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:
7.

図書

図書
N.B. Karayiannis, A.N. Venetsanopoulos
出版情報: Boston : Kluwer Academic, c1993  xii, 440 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 209
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