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Hybrid Neural Systems

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Cover of 'Hybrid Neural Systems'

Table of Contents

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    Book Overview
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    Chapter 1 An Overview of Hybrid Neural Systems
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    Chapter 2 Layered Hybrid Connectionist Models for Cognitive Science
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    Chapter 3 Types and Quantifiers in SHRUTI – A Connectionist Model of Rapid Reasoning and Relational Processing
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    Chapter 4 A Recursive Neural Network for Reflexive Reasoning
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    Chapter 5 A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning
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    Chapter 6 Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference
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    Chapter 7 Towards a Hybrid Model of First-Order Theory Refinement
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    Chapter 8 Dynamical Recurrent Networks for Sequential Data Processing
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    Chapter 9 Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective
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    Chapter 10 Combining Maps and Distributed Representations for Shift-Reduce Parsing
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    Chapter 11 Towards Hybrid Neural Learning Internet Agents
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    Chapter 12 A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations
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    Chapter 13 Large Patterns Make Great Symbols: An Example of Learning from Example
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    Chapter 14 Context Vectors: A Step Toward a “Grand Unified Representation”
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    Chapter 15 Integration of Graphical Rules with Adaptive Learning of Structured Information
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    Chapter 16 Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks
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    Chapter 17 Symbolic Rule Extraction from the DIMLP Neural Network
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    Chapter 18 Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics
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    Chapter 19 Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification
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    Chapter 20 High Order Eigentensors as Symbolic Rules in Competitive Learning
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    Chapter 21 Holistic Symbol Processing and the Sequential RAAM: An Evaluation
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    Chapter 22 Life, Mind, and Robots
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    Chapter 23 Supplementing Neural Reinforcement Learning with Symbolic Methods
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    Chapter 24 Self-Organizing Maps in Symbol Processing
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    Chapter 25 Evolution of Symbolisation: Signposts to a Bridge between Connectionist and Symbolic Systems
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    Chapter 26 A Cellular Neural Associative Array for Symbolic Vision
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    Chapter 27 Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots
Attention for Chapter 14: Context Vectors: A Step Toward a “Grand Unified Representation”
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Chapter title
Context Vectors: A Step Toward a “Grand Unified Representation”
Chapter number 14
Book title
Hybrid Neural Systems
Published by
Springer, Berlin, Heidelberg, December 1998
DOI 10.1007/10719871_14
Book ISBNs
978-3-54-067305-7, 978-3-54-046417-4
Authors

Stephen I. Gallant, Gallant, Stephen I.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 14%
Unknown 6 86%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 43%
Student > Ph. D. Student 1 14%
Student > Bachelor 1 14%
Researcher 1 14%
Student > Postgraduate 1 14%
Other 0 0%
Readers by discipline Count As %
Computer Science 4 57%
Linguistics 1 14%
Business, Management and Accounting 1 14%
Engineering 1 14%