close
1.

図書

図書
Larry R. Medsker
出版情報: Boston : Kluwer Academic, c1994  240 p. ; 25 cm
所蔵情報: loading…
2.

図書

図書
Bart Kosko
出版情報: Englewood Cliffs, N.J. : Prentice Hall, c1992  xxvii, 449 p. ; 24 cm
所蔵情報: loading…
3.

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
所蔵情報: loading…
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:
文献の複写および貸借の依頼を行う
 文献複写・貸借依頼