Hybrid Neural Systems : Lecture Notes in Artificial Intelligence - Stefan Wermter

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Hybrid Neural Systems

By: Stefan Wermter (Editor), Ron Sun (Editor)

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Paperback | 1 May 2000

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420 Pages

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24.13 x 15.88 x 2.54

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Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems.
The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches.

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You Can Find This Book In

Non-Fiction Medicine Clinical & Internal Medicine Neurology & Clinical Neurophysiology Computing & I.T.Computer Science Computer Architecture & Logic Design Science Biology, Life Sciences Life Sciences in General Neurosciences Psychology

Cognition & Cognitive Psychology Artificial Intelligence Neural Networks & Fuzzy Systems

An Overview of Hybrid Neural Systems	p. 1
Structured Connectionism and Rule Representation
Layered Hybrid Connectionist Models for Cognitive Science	p. 14
Types and Quantifiers in SHRUTI: A Connectionist Model of Rapid Reasoning and Relational Processing	p. 28
A Recursive Neural Network for Reflexive Reasoning	p. 46
A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning	p. 63
Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference	p. 78
Towards a Hybrid Model of First-Order Theory Refinement	p. 92
Distributed Neural Architectures and Language Processing
Dynamical Recurrent Networks for Sequential Data Processing	p. 107
Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective	p. 123
Combining Maps and Distributed Representations for Shift-Reduce Parsing	p. 144
Towards Hybrid Neural Learning Internet Agents	p. 158
A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations	p. 175
Large Patterns Make Great Symbols: An Example of Learning from Example	p. 194
Context Vectors: A Step Toward a "Grand Unified Representation"	p. 204
Integration of Graphical Rules with Adaptive Learning of Structured Information	p. 211
Transformation and Explanation
Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks	p. 226
Symbolic Rule Extraction from the DIMLP Neural Network	p. 240
Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics	p. 255
Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification	p. 270
High Order Eigentensors as Symbolic Rules in Competitive Learning	p. 286
Holistic Symbol Processing and the Sequential RAAM: An Evaluation	p. 298
Robotics, Vision and Cognitive Approaches
Life, Mind, and Robots: The Ins and Outs of Embodied Cognition	p. 313
Supplementing Neural Reinforcement Learning with Symbolic Methods	p. 333
Self-Organizing Maps in Symbol Processing	p. 348
Evolution of Symbolization: Signposts to a Bridge Between Connectionist and Symbolic Systems	p. 363
A Cellular Neural Associative Array for Symbolic Vision	p. 372
Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots	p. 387
Author Index	p. 403
Table of Contents provided by Publisher. All Rights Reserved.

Hybrid Neural Systems

At a Glance

Paperback

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How to return your order

Defective items

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