Hybrid Neural Systems / edited by Stefan Wermter, Ron Sun.

Format:

Book

Contributor:

Wermter, Stefan, editor.

Sun, Ron, 1960- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1778.

Lecture Notes in Artificial Intelligence ; 1778

Language:

English

Subjects (All):

Neurosciences.

Artificial intelligence.

Computers.

Microprocessors.

Artificial Intelligence.

Computation by Abstract Devices.

Processor Architectures.

Local Subjects:

Neurosciences.

Artificial Intelligence.

Computation by Abstract Devices.

Processor Architectures.

Physical Description:

1 online resource (XI, 401 pages).

Edition:

First edition 2000.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.

System Details:

text file PDF

Summary:

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.

Contents:

An Overview of Hybrid Neural Systems

An Overview of Hybrid Neural Systems

Structured Connectionism and Rule Representation

Layered Hybrid Connectionist Models for Cognitive Science

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

A Recursive Neural Network for Reflexive Reasoning

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

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

Towards a Hybrid Model of First-Order Theory Refinement

Distributed Neural Architectures and Language Processing

Dynamical Recurrent Networks for Sequential Data Processing

Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective

Combining Maps and Distributed Representations for Shift-Reduce Parsing

Towards Hybrid Neural Learning Internet Agents

A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations

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

Context Vectors: A Step Toward a "Grand Unified Representation"

Integration of Graphical Rules with Adaptive Learning of Structured Information

Transformation and Explanation

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

Symbolic Rule Extraction from the DIMLP Neural Network

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

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

High Order Eigentensors as Symbolic Rules in Competitive Learning

Holistic Symbol Processing and the Sequential RAAM: An Evaluation

Robotics, Vision and Cognitive Approaches

Life, Mind, and Robots

Supplementing Neural Reinforcement Learning with Symbolic Methods

Self-Organizing Maps in Symbol Processing

Evolution of Symbolisation: Signposts to a Bridge between Connectionist and Symbolic Systems

A Cellular Neural Associative Array for Symbolic Vision

Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots.

Other Format:

Printed edition:

ISBN:

978-3-540-46417-4

9783540464174

Access Restriction:

Restricted for use by site license.