Del 0 - Physics of Neural Networks

Models of Neural Networks III

Association, Generalization, and Representation

Inbunden, Engelska, 1995

AvEytan Domany,J. Leo van Hemmen,Klaus Schulten,J. Leo Van Hemmen,J Leo van Hemmen

1 429 kr

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This collection of articles by leading researchers in neural networks responds to the urgent need for timely and comprehensive reviews in a multidisciplinary, rapidly developing field of research. It continues the themes of the previous volume, but shifts its focus to more practical matters, such as data storage and retrieval, and the recognition of handwriting.

Produktinformation

Utgivningsdatum1995-12-01
Mått155 x 235 x 25 mm
Vikt633 g
FormatInbunden
SpråkEngelska
SeriePhysics of Neural Networks
Antal sidor311
Upplaga1996
FörlagSpringer-Verlag New York Inc.
ISBN9780387943688

Tillhör följande kategorier

1. Global Analysis of Recurrent Neural Networks.- 1.1 Global Analysis-Why?.- 1.2 A Framework for Neural Dynamics.- 1.3 Fixed Points.- 1.4 Periodic Limit Cycles and Beyond.- 1.5 Synchronization of Action Potentials.- 1.6 Conclusions.- References.- 2. Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns.- 2.1 Introduction.- 2.2 Correlation-Based Models.- 2.3 The Problem of Map Structure.- 2.4 The Computational Significance of Correlatin-Based Rules.- 2.5 Open Questions.- References.- 3. Associative Data Storage and Retrieval in Neural Networks.- 3.1 Introduction and Overview.- 3.1.1 Memory and Representation.- 3.2 Neural Associatve Memory Models.- 3.3 Analysis of the Retrieval Process.- 3.4 Information Theory of the Memory Process.- 3.5 Model Performance.- 3.6 Discussion.- Appendix 3.1.- Appendix 3.2.- References.- 4. Inferences Modeled with Neural Networks.- 4.1 Introduction.- 4.2 Model for Cognitive Systems and for Experiences.- 4.3 Inductive Inference.- 4.4 External Memory.- 4.5 Limited Use of External Memory.- 4.6 Deductive Inference.- 4.7 Conclusion.- References.- 5. Statistical Mechanics of Generalization.- 5.1 Introduction.- 5.2 General Results.- 5.3 The Perceptron.- 5.4 Geometry in Phase Space and Asymptotic Scaling.- 5.5 Applications to Perceptrons.- 5.6 Summary and Outlook.- Appendix 5.1: Proof of Sauer’s Lemma.- Appendix 5.2: Order Parameters for ADALINE.- References.- 6. Bayesian Methods for Backpropagation Networks.- 6.1 Probability Theory and Occam’s Razor.- 6.2 Neural Networks as Probabilistic Models.- 6.3 Setting Regularization Constants ? and ?.- 6.4 Model Comparison.- 6.5 Error Bars and Predictions.- 6.6 Pruning.- 6.7 Automatic Relevance Determination.- 6.8 Implicit Priors.- 6.9 Cheap and CheerfulImplementations.- 6.10 Discussion.- References.- 7. Penacée: A Neural Net System for Recognizing On-Line Handwriting.- 7.1 Introduction.- 7.2 Description of the Building Blocks.- 7.3 Applications.- 7.4 Conclusion.- References.- 8. Topology Representing Network in Robotics.- 8.1 Introduction.- 8.2 Problem Description.- 8.3 Topology Representing Network Algorithm.- 8.4 Experimental Results and Discussion.- References.

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