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Machine Learning List Vol. 1 No. 13
Machine Learning List: Vol. 1 No. 13
Saturday, Nov 11, 1989
Contents:
Quality Prediction
EKAW
Bibliographies
The Machine Learning List is moderated. Contributions should be relevant to
the scientific study of machine learning. Mail contributions to ml@ics.uci.edu.
Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues
of Volume 1 may be FTP'd from /usr2/spool/ftp/pub/ml-list/V1/<N> or N.Z where
N is the number of the issue; Host ics.uci.edu; Userid & password: anonymous
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Date: Fri, 3 Nov 89 11:50:06 +0100
From: Fred Tusveld <hcsrnd!tusveld@relay.EU.NET>
Subject: quality prediction
I am working in an Esprit project concerned with the development of a quality
support environment. Some of the hard problems involve the prediction of
quality on the basis of process data and/or simulation of the processing
environment. Approaches using explanation based learning, inductive learning
and analogical reasoning are considered.
Does anybody know of applications of these in the area of quality control
in an industrial environment?
Any information will be appreciated.
Fred Tusveld
Research and Development
HCS Information Technology
Landdrostlaan 51, 7302 HA Apeldoorn
The Netherlands
Email: hp4nl!hcsrnd!tusveld
----------------------------------------------------------------------
From: john@bcsaic (John Boose)
Subject: CFP 1990 European Knowledge Acquisition Workshop
Date: 30 Oct 89 01:00:19 GMT
[From news.announce.conferences]
CALL FOR PAPERS
4th European Knowledge Acquisition for Knowledge-Based
Systems Workshop
EKAW-90
Amsterdam, The Netherlands
June 25-29, 1990
The EKAW is concerned with the research on acquisition of knowledge for
practical knowledge-based systems. The workshop will be in two parts: a
one day open meeting with key note presentations, and a three-and-a-half
day closed workshop. The closed workshop will be limited to 40
participants, one author for each paper accepted. Papers are invited
for consideration on all aspects of knowledge acquisition for
knowledge-based systems: including (but not restricted to):
* Elicitation/modeling of expertise - systems that obtain and model
knowledge from experts.
* Elicitation/modeling of expertise - manual knowledge acquisition
methods and techniques.
* Apprenticeship, explanation-based, and other learning systems;
integration of such systems with other knowledge acquisition techniques.
* Issues in cognition and expertise that affect the knowledge
acquisition process.
* Extracting and modeling of knowledge from text.
* Integration of knowledge acquisition techniques within a single
system; integration of knowledge acquisition systems with other systems
(hypermedia, database management systems, simulators, spreadsheets).
* Knowledge acquisition methodology and training.
* Validation of knowledge acquisition techniques; the role of knowledge
acquisition techniques in validating knowledge-based systems.
SUBMISSION OF PAPERS
Five copies of a full-length draft paper (up to 20 pages) should be sent
to Bob Wielinga (see address below) before February 26th, 1990.
Acceptance notices will be mailed by April 16th. Camera-ready copies
should be returned before May 14th. The proceedings will be distributed
at the workshop.
WORKSHOP CO-CHAIRMEN
Bob Wielinga
Social Science Informatics,
University of Amsterdam,
Herengracht 196, 1016 BS Amsterdam,
The Netherlands,
Tel: +31 20 525 2160/2073
E-mail: wielinga@swivax.UUCP
(will change to wielinga@swi.psy.uva.nl)
John Boose
Advanced Technology Center
Boeing Computer Services, 7L-64
PO Box 24346
Seattle, Washington, USA 98124,
Tel: (206) 865-3253
E-mail: john@atc.boeing.com
Brian Gaines
Department of Computer Science
University of Calgary
2500 University Dr. NW
Calgary, Alberta, Canada T2N 1N4,
Tel: (403) 220-5901
E-mail: gaines@cpsc.calgary.ca
PROGRAM COMMITTEE
Tom Addis, University of Reading
Guy Boy, Centre d'Etudes et de Recherche de Toulouse
Jeffrey Bradshaw, Boeing Computer Services
Jean Gabriel Ganascia, Universite Paris-Sud
Yves Kodratoff, Universite Paris-Sud
Marc Linster, GMD, St Augustin
John McDermott, Digital Equipment Corporation
Ryszard Michalski, George Mason University
Katharina Morik, GMD, St Augustin
Nigel Shadbolt, University of Nottingham
Mildred Shaw, University of Calgary
Guus Schreiber, University of Amsterdam
Maarten van Someren, University of Ansterdam
----------------------------------------------------------------------
Date: Wed, 1 Nov 89 16:20:23 EST
From: "Alberto M. Segre" <segre@cs.cornell.EDU>
Subject: Bibliographies
Here's all of the Machine Learing Journal articles. Enjoy!
# This is a shell archive.
# Remove everything above and including the cut line.
# Then run the rest of the file through sh.
#----cut here-----cut here-----cut here-----cut here----#
#!/bin/sh
# shar: Shell Archiver
# Run the following text with /bin/sh to create:
# bibinc.ml3
# mlj.ref
# This archive created: Wed Nov 1 16:19:54 1989
cat << \SHAR_EOF > bibinc.ml3
#
# journals
#
D ML Machine Learning
#
# months
#
D JAN January
D FEB February
D MAR March
D APR April
D MAY May
D JUN June
D JUL July
D AUG August
D SEP September
D OCT October
D NOV November
D DEC December
SHAR_EOF
cat << \SHAR_EOF > mlj.ref
%T On Machine Learning
%A P. Langley
%J ML
%V 1
%N 1
%P 5-10
%D 1986
%T Chunking in SOAR: The Anatomy of a General Learning Mechanism
%A J.E. Laird
%A P.S. Rosenbloom
%A A. Newell
%J ML
%V 1
%N 1
%P 11-46
%D 1986
%T Explanation-Based Generalization: A Unifying View
%A T.M. Mitchell
%A R.M. Keller
%A S.T. Kedar-Cabelli
%J ML
%V 1
%N 1
%P 47-80
%D 1986
%T Induction of Decision Trees
%A J.R. Quinlan
%J ML
%V 1
%N 1
%P 81-106
%D 1986
%T A Theory of Historical Discovery: The Construction of Componential Models
%A J.M. Zytkow
%A H.A. Simon
%J ML
%V 1
%N 1
%P 107-137
%D 1986
%T The Terminology of Machine Learning
%A P. Langley
%J ML
%V 1
%N 2
%P 141-144
%D 1986
%T Explanation-Based Learning: An Alternative View
%A G. DeJong
%A R. Mooney
%J ML
%V 1
%N 2
%P 145-176
%D 1986
%T A General Framework for Induction and a Study of Selective Induction
%A L. Rendell
%J ML
%V 1
%N 2
%P 177-226
%D 1986
%T Human and Machine Learning
%A P. Langley
%J ML
%V 1
%N 3
%P 243-248
%D 1986
%T Experimental Goal Regression: A Method for Learning Problem-solving Heuristics
%A B.W. Porter
%A D.F. Kibler
%J ML
%V 1
%N 3
%P 249-286
%D 1986
%T Learning at the Knowledge Level
%A T.G. Dietterich
%J ML
%V 1
%N 3
%P 287-316
%D 1986
%T Incremental Learning From Noisy Data
%A J.C. Schlimmer
%A R.H. Granger, Jr.
%J ML
%V 1
%N 3
%P 317-354
%D 1986
%T Machine Learning and Discovery
%A P. Langley
%A R.S. Michalski
%J ML
%V 1
%N 4
%P 363-366
%D 1986
%T Integrating Quantitative and Qualitative Discovery: The ABACUS System
%A B.C. Falkenhainer
%A R.S. Michalski
%J ML
%V 1
%N 4
%P 367-402
%D 1986
%T Determining Arguments of Invariant Functional Descriptions
%A M.M. Kokar
%J ML
%V 1
%N 4
%P 403-422
%D 1986
%T Chemical Discovery as Belief Revision
%A D. Rose
%A P. Langley
%J ML
%V 1
%N 4
%P 423-452
%D 1986
%T Machine Learning and Grammar Induction
%A P. Langley
%J ML
%V 2
%N 1
%P 5-8
%D 1986
%T Learning Syntax by Automata Induction
%A R.C. Berwick
%A S. Pilato
%J ML
%V 2
%N 1
%P 9-38
%D 1987
%T A Version Space Approach to Learning Context-Free Grammars
%A K. VanLehn
%A W. Ball
%J ML
%V 2
%N 1
%P 39-74
%D 1987
%T Machine Learning and Concept Formation
%A P. Langley
%J ML
%V 2
%N 2
%P 99-102
%D SEP 1987
%T Experiments with Incremental Concept Formation:UNIMEM
%A M. Lebowitz
%J ML
%V 2
%N 2
%P 103-138
%D SEP 1987
%T Knowledge Acquisition Via Incremental Conceptual Clustering
%A D.H. Fisher
%J ML
%V 2
%N 2
%P 139-172
%D SEP 1987
%T A Review of the Fourth International Workshop on Machine Learning
%A R.P. Hall
%A B. Falkenhainer
%A N. Flann
%J ML
%V 2
%N 2
%P 173-190
%D SEP 1987
%T Research Papers in Machine Learning
%A P. Langley
%J ML
%V 2
%N 3
%P 195-198
%D NOV 1987
%T Classifier Systems and the Animat Problem
%A S.W. Wilson
%J ML
%V 2
%N 3
%P 199-228
%D NOV 1987
%T Learning Decision Lists
%A R.L. Rivest
%J ML
%V 2
%N 3
%P 229-246
%D NOV 1987
%T Theory Change Via View Application in Instructionless Learning
%A J. Shrager
%J ML
%V 2
%N 3
%P 247-276
%D NOV 1987
%T New Theoretical Directions in Machine Learning
%A D. Haussler
%J ML
%V 2
%N 4
%P 281-284
%D APR 1988
%T Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
%A N. Littlestone
%J ML
%V 2
%N 4
%P 285-318
%D APR 1988
%T Queries and Concept Learning
%A D. Angluin
%J ML
%V 2
%N 4
%P 319-342
%D APR 1988
%T Learning From Noisy Examples
%A D. Angluin
%A P. Laird
%J ML
%V 2
%N 4
%P 343-370
%D APR 1988
%T Criteria for Polynomial-Time (Conceptual) Clustering
%A L. Pitt
%A R. Reinke
%J ML
%V 2
%N 4
%P 371-396
%D APR 1988
%T Machine Learning as an Experimental Science
%A P. Langley
%J ML
%V 3
%N 1
%P 5-8
%D AUG 1988
%T Learning to Predict by the Methods of Temporal Differences
%A R. Sutton
%J ML
%V 3
%N 1
%P 9-44
%D AUG 1988
%T Learning by Failing to Explain: Using Partial Explanations to Learn in Incomplete or Intractable Domains
%A R. Hall
%J ML
%V 3
%N 1
%P 45-78
%D AUG 1988
%T A Review of Machine Learning at AAAI-87
%A R. Greiner
%A B. Silver
%A S. Becker
%A M. Gruninger
%J ML
%V 3
%N 1
%P 79-92
%D AUG 1988
%T Genetic Algorithms and Machine Learning
%A D.E. Goldberg
%A J.H. Holland
%J ML
%V 3
%N 2/3
%P 95-100
%D OCT 1988
%T Genetic Algorithms in Noisy Environments
%A J.M. Fitzpatrick
%A J.J. Grefenstette
%J ML
%V 3
%N 2/3
%P 101-120
%D OCT 1988
%T Learning With Genetic Algorithms: An Overview
%A K. DeJong
%J ML
%V 3
%N 2/3
%P 121-138
%D OCT 1988
%T A Tale of Two Classifier Systems
%A G.G. Robertson
%A R.L. Riolo
%J ML
%V 3
%N 2/3
%P 139-160
%D OCT 1988
%T Classifier Systems That Learn Internal World Models
%A L.B. Booker
%J ML
%V 3
%N 2/3
%P 161-192
%D OCT 1988
%T Learning and Programming in Classifier Systems
%A R.K. Belew
%A S. Forrest
%J ML
%V 3
%N 2/3
%P 193-224
%D OCT 1988
%T Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms
%A J.J. Grefenstette
%J ML
%V 3
%N 2/3
%P 225-246
%D OCT 1988
%T Toward a Unified Science of Machine Learning
%A P. Langley
%J ML
%V 3
%N 4
%P 253-260
%D MAR 1989
%T The CN2 Induction Algorithm
%A P. Clark
%A T. Niblett
%J ML
%V 3
%N 4
%P 261-284
%D MAR 1989
%T A Heuristic Approach to the Discovery of Macro-Operators
%A G.A. Iba
%J ML
%V 3
%N 4
%P 285-318
%D MAR 1989
%T An Empirical Comparison of Selected Measures for Decision-Tree Induction
%A J. Mingers
%J ML
%V 3
%N 4
%P 319-342
%D MAR 1989
%T Conceptual Clustering, Categorization, and Polymorphy
%A S.J. Hanson
%A M. Bauer
%J ML
%V 3
%N 4
%P 343-372
%D MAR 1989
SHAR_EOF
# End of shell archive
exit 0
----------------------------------------------------------------------
END of ML-LIST 1.13
