Hybrid Neural Systems

Editors:

ISBN: 978-3-540-67305-7 (Print) 978-3-540-46417-4 (Online)

Table of contents (27 chapters)

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  1. Front Matter

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  2. An Overview of Hybrid Neural Systems

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      Pages 1-13

      An Overview of Hybrid Neural Systems

  3. Structured Connectionism and Rule Representation

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      Pages 14-27

      Layered Hybrid Connectionist Models for Cognitive Science

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      Pages 28-45

      Types and Quantifiers in SHRUTI – A Connectionist Model of Rapid Reasoning and Relational Processing

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      Pages 46-62

      A Recursive Neural Network for Reflexive Reasoning

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      Pages 63-77

      A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning

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      Pages 78-91

      Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference

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      Pages 92-106

      Towards a Hybrid Model of First-Order Theory Refinement

  4. Distributed Neural Architectures and Language Processing

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      Pages 107-122

      Dynamical Recurrent Networks for Sequential Data Processing

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      Pages 123-143

      Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective

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      Pages 144-157

      Combining Maps and Distributed Representations for Shift-Reduce Parsing

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      Pages 158-174

      Towards Hybrid Neural Learning Internet Agents

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      Pages 175-193

      A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations

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      Pages 194-203

      Large Patterns Make Great Symbols: An Example of Learning from Example

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      Pages 204-210

      Context Vectors: A Step Toward a “Grand Unified Representation”

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      Pages 211-225

      Integration of Graphical Rules with Adaptive Learning of Structured Information

  5. Transformation and Explanation

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      Pages 226-239

      Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks

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      Pages 240-254

      Symbolic Rule Extraction from the DIMLP Neural Network

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      Book Chapter

      Pages 255-269

      Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics

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      Book Chapter

      Pages 270-285

      Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification

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      Book Chapter

      Pages 286-297

      High Order Eigentensors as Symbolic Rules in Competitive Learning

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