LINGUIST List 11.1026

Fri May 5 2000

Books: Neural Linguistics and Cognitive Linguistics

Editor for this issue: Scott Fults <scottlinguistlist.org>


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  1. Buerk, Anita, Neural Ling & Cognitive Ling: Hybrid Neural Systems, S. Wermter

Message 1: Neural Ling & Cognitive Ling: Hybrid Neural Systems, S. Wermter

Date: Fri, 5 May 2000 11:48:47 +0200
From: Buerk, Anita <BuerkSpringer.de>
Subject: Neural Ling & Cognitive Ling: Hybrid Neural Systems, S. Wermter


NEW BOOK ON HYBRID NEURAL SYSTEMS FROM Springer
===============================================

Wermter, S., University of Sunderland, UK
Sun, R., University of Missouri, Columbia, MO (Eds.)
Hybrid Neural Systems
2000. IX, 403 pp.
3-540-67305-9
DM 86,- / US$
LNCS 1778
 
More details on this book Hybrid Neural Systems can be gained from its web
page http://www.his.sunderland.ac.uk/newbook/hybrid.html
(all abstracts and first chapter)
 
Overview
--------
Keywords: Artificial Neural Networks, Hybrid Neural Systems,
Connectionism, Hybrid Symbolic Neural Architectures, Cognitive
Neuroscience, Machine Learning, Language Processing
 
The aim of this book is to present a broad spectrum of current
research in hybrid neural systems, and advance the state of the art in
neural networks and artificial intelligence. Hybrid neural systems are
computational systems which are based mainly on artificial neural
networks but which also allow a symbolic interpretation or interaction
with symbolic components.

This book focuses on the following issues related to different types
of representation: How does neural representation contribute to the
success of hybrid systems? How does symbolic representation
supplement neural representation? How can these types of
representation be combined? How can we utilize their interaction and
synergy? How can we develop neural and hybrid systems for new
domains? What are the strengths and weaknesses of hybrid neural
techniques? Are current principles and methodologies in hybrid neural
systems useful? How can they be extended? What will be the impact of
hybrid and neural echniques in the future?
 
Table of Contents
------------------
An Overview of Hybrid Neural Systems
Stefan Wermter and Ron Sun

Structured Connectionism and Rule Representation
--------------------------------
Layered Hybrid Connectionist Models for Cognitive Science
Jerome Feldman and David Bailey
 
Types and Quantifiers in SHRUTI --- A Connectionist Model of Rapid
Reasoning and Relational Processing
Lokendra Shastri
 
A Recursive Neural Network for Reflexive Reasoning
Steffen H�lldobler, Yvonne Kalinke and J�rg Wunderlich
 
A Novel Modular Neural Architecture for Rule-based and
Similarity-based Reasoning
Rafal Bogacz and Christophe Giraud-Carrier
 
Addressing Knowledge-Representation Issues in Connectionist Symbolic
Rule Encoding for General Inference
Nam Seog Park
 
Towards a Hybrid Model of First-Order Theory Refinement
Nelson A. Hallack, Gerson Zaverucha and Valmir C. Barbosa
 

Distributed Neural Architectures and Language Processing
-------------------------------------
Dynamical Recurrent Networks for Sequential Data Processing
Stefan Kremer and John Kolen
 
Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems
Perspective
Christian W. Omlin, Lee Giles and Karvel K. Thornber

Combining Maps and Distributed Representations for Shift-Reduce
Parsing Marshall R. Mayberry and Risto Miikkulainen

Towards Hybrid Neural Learning Internet Agents
Stefan Wermter, Garen Arevian and Christo Panchev

A Connectionist Simulation of the Empirical Acquisition of Grammatical
Relations

---------------------------------------------

William C. Morris, Garrison W. Cottrell and Jeffrey L. Elman
Large Patterns Make Great Symbols: An Example of Learning from
Example
Pentti Kanerva
 
Context Vectors: A Step Toward a Grand Unified Representation
Stephen I. Gallant
 
Integration of Graphical Rules with Adaptive Learning of Structured
Information
Paolo Frasconi, Marco Gori and Alessandro Sperduti

Transformation and Explanation

--------------------
 
Lessons from Past, Current Issues and Future Research Directions in
Extracting the Knowledge Embedded in Artificial Neural Networks
Alan B. Tickle, Frederic Maire, Guido Bologna, Robert Andrews and Joachim
Diederich

Symbolic Rule Extraction from the DIMLP Neural Network
Guido Bologna
 
Understanding State Space Organization in Recurrent Neural Networks
with Iterative Function Systems Dynamics
Peter Tino, Georg Dorffner and Christian Schittenkopf
 
Direct Explanations and Knowledge Extraction from a Multilayer
Perceptron Network that Performs Low Back Pain Classification
Marilyn L. Vaughn, Steven J. Cavill, Stewart J. Taylor, Michael A. Foy
and Anthony J.B. Fogg

High Order Eigentensors as Symbolic Rules in Competitive Learning
Hod Lipson and Hava T. Siegelmann
 
Holistic Symbol Processing and the Sequential RAAM: An Evaluation
James A. Hammerton and Barry L. Kalman
 
Robotics, Vision and Cognitive Approaches
-------------------------------------------

Life, Mind and Robots: The Ins and Outs of Embodied Cognition
Noel Sharkey and Tom Ziemke
 
Supplementing Neural Reinforcement Learning with Symbolic Methods
Ron Sun
 
Self-Organizing Maps in Symbol Processing
Timo Honkela
 
Evolution of Symbolisation: Signposts to a Bridge between
Connectionist and Symbolic Systems
Ronan Reilly
 
A Cellular Neural Associative Array for Symbolic Vision
Christos Orovas and James Austin
 
Application of Neurosymbolic Integration for Environment Modelling in
Mobile Robots
Gerhard K. Kraetzschmar, Stefan Sablatn�g, Stefan Enderle, G�nther Palm
 
======================================================
 
Online order
 --------
http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67305-9
 
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mailto:		ordersspringer-ny.com
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