Try the following books in the field of Soft Computing, in which JESS is
under this
category:
FIRST BOOK
----------
Intelligent Systems and Soft Computing: Prospects, Tools and Applications
Behnam Azvine, Nader Azarmi, Detlef D. Nauck (Eds.)
Published July 2000. 357 pp. ISBN 3-540-67837-9, DM 84,-
Springer LNAI State of the Art Survey 1804
Fields: Soft Computing; Artificial Intelligence; Software Engineering;
Written for: Researchers and professionals
Table of contents
Introduction
B. Azvine, N. Azarmi, D. Nauck
Section 1: Prospects
>From Computing with Numbers to Computing with Words, From Manipulation of
Measurements to Manipulation of Perceptions
L.A. Zadeh
Bringing AI and Soft Computing Together: A Neurobiological Perspective
J.G. Taylor
Future Directions for Soft Computing
J.F. Baldwin
Problems and Prospects in Fuzzy Data Analysis
R. Kruse, C. Borgelt and D. Nauck
Soft Agent Computing: Towards Enhancing Agent Technology with Soft Computing
E.H. Mamdani, A.G. Sichanie and J. Pitt
Section 2: Tools
NEFCLASS-J: A JAVA-based Soft Computing Tool
D. Nauck and R. Kruse
Soft Computing for Intelligent Knowledge-based Systems
T.P. Martin and J.F. Baldwin
Advanced Fuzzy Clustering and Decision Tree Plug-Ins for DataEngine
C. Borgelt and H. Timm
Section 3: Applications
The Intelligent Assistant: An Overview
B. Azvine, D. Djian, K.C. Tsui and W. Wobcke
The YPA: An Assistant for Classified Directory Enquiries
A. De Roeck, U. Kruschwitz, P. Scott, S. Steel, R. Turner and N. Webb
An Intelligent Multi-modal Interface
K.C. Tsui, B. Azvine and D. Djian
Communication Management: E-mail and Telephone Assistants
D. Djian
Time Management in the Intelligent Assistant
W. Wobcke
Machine Interpretation of Facial Expressions
S.J. Case, J.F. Baldwin and T.P. Martin
SECOND BOOK
-----------
Soft Computing in Case Based Reasoning
Edited by: S. K. Pal, T. S. Dillon and D. S. Yeung
Soft Computing in Case Based Reasoning demonstrates
how various soft computing tools can be applied to design
and develop methodologies and systems with case based
reasoning for real-life decision-making or recognition
problems.
Comprising of contributions from experts from all
over the world, it:
* provides an introduction to CBR and soft
computing, and the relevance of their integration;
* evaluates the strengths and weaknesses of CBT in
its current form;
* presents recent developments and significant
applications in domain such as data-mining, medical
diagnosis, knowledge-based expert systems, banking and
forensic investigation;
* addresses new information on developing
intelligent systems.
Soft Computing in Case Based Reasoning is of
particular interest to graduate students and researchers
in computer science, electrical engineering and
information technology but it is also of interest to
researchers and practitioners in the fields of systems design,
machine intelligence, pattern recognition and data mining.
400 pages Softcover ISBN: 1-85233-262-X
Recommended Retail Price: ?45.00*, $69.75
THIRD BOOK
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"Rough set data analysis - A road to non-invasive knowledge discovery"
Methodos Publishers (UK), 2000, ISBN 190328001X, 107pp, GBP 10.00, Euro
15.00
It can be ordered by your bookstore or directly from the publisher's web
page
www.methodos.eu.com
where more details can be found.
Best regards,
Ivo Duentsch
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-----
Abstract:
This is not the first book on rough set analysis and certainly not the first
book on knowledge discovery algorithms, but it is the first attempt to do
this in a non-invasive way.
The term "non-invasive" in connection with knowledge discovery or data
analysis is new and needs some introductory remarks. We have worked from
about 1993 on topics of knowledge discovery and/or data analysis (both
topics
are sometimes hard to distinguish), and we felt that most of the common work
on this topics was based on at least discussable assumptions. We regarded
the
invention of Rough Set Data Analysis (RSDA) as one of the big events in
those
days, because, at the start, RSDA was clearly structured, simple, and
straightforward from basic principles to effective data analysis. It is our
conviction that a model builder who uses a structural and/or statistical
system should be clear about the basic assumptions of the model.
Furthermore,
it seems to be a wise strategy to use models with only a few
(pre-)assumptions about the data. If both characteristics are fulfilled, we
call a modelling process non-invasive. This idea is not really new, because
the non-parametric statistics approach based on the motto of R.A.Fisher
Let the data speak for themselves,
can be transferred to the context of knowledge discovery. It is no wonder
that e.g. the randomisation procedure (one of the flagships of
non-parametric
statistics) is part of the non-invasive knowledge discovery approach.
In this book we present an overview of the work we have done in the past
seven years on the foundations and details of data analysis. During this
time, we have learned to look at data analysis from many different angles,
and we have tried not to be biased for - or against - any particular method,
although our ideas take a prominent part of this book. In addition, we have
included many citations of papers on RSDA in knowledge discovery by other
research groups as well to somewhat alleviate the emphasis on our own work.
We hope that the presentation is neither too rough nor too fuzzy, so that
the
reader can discover some knowledge in this book
Contents:
1. Introduction
2. Data models and model assumptions
3. Basic rough set data analysis
3.1 Fundamentals
3.2 Approximation quality
3.3 Information systems I
3.4 Indiscernability relations
3.5 Feature selection
3.6 Discernability matrices and Boolean reasoning
3.7 Rules
3.8 Approximation quality of attribute sets
4. Rule significance
4.1 Significant and casual rules
4.2 Conditional significance
4.3 Sequential randomisation
5. Data discretisation
5.1 Classificatory discretisation
5.2 Discretisation of real valued attributes
6. Model selection
6.1 Dynamic reducts
6.2 Rough entropy measures
6.3 Entropy measures and approximation quality
7. Probabilistic granule analysis
7.1 The variable precision model
7.2 Replicated decision systems
7.3 An algorithm to find probabilistic rules
7.4 Unsupervised learning and nonparametric distribution estimates
8. Imputation
8.1 Statistical procedures
8.2 Imputation from known values
9.0 Beyond rough sets
9.1 Relational attribute systems
9.2 Non-invasive test theory
10. Epilogue
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Cheers.
Sione.
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]
Sent: Friday, April 06, 2001 2:29 PM
To: [EMAIL PROTECTED]
Subject: Re: JESS: Resources
Being new to the field, I am also interested. I would also like to know if
anyone recommends a good book on Java Beans. Thanks
MJ
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