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Machine Learning List Vol. 4 No. 13
Machine Learning List: Vol. 4 No. 13
Tuesday, June 23, 1992
Contents:
Job advertisement: SIEMENS
CLNL workshop
Workshop schedule: BIASES IN INDUCTIVE LEARNING
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Date: Thu, 18 Jun 92 19:12:32 EDT
From: G Drastal <drastal@learning.siemens.COM>
Subject: Job advertisement: SIEMENS
SIEMENS
Research Scientists are sought for the
Symbolic Methods and Knowledge Acquisition Group
within the Learning Systems Department, Siemens Corporate Research (SCR).
SCR is the U.S. based corporate research lab of Siemens A.G., a global company
with worldwide sales of about $40 billion ($4 billion in U.S.). We are located
in a suburb of Princeton, New Jersey, a few minutes from Princeton University
and about an hour from either New York City or Philadelphia.
The Learning Systems Department has primary responsibility for Siemens
worldwide in the areas of learning algorithms and architectures for problems
in Pattern classification, Visual processing, Sensorimotor control,
Problem solving, Knowledge acquisition for problem solving systems,
Adaptive information retrieval, and other areas. The actual mixture changes
as individual researchers join our department and exert their influence.
The Symbolic Methods group is particularly interested in the use of inductive
AI methods in creating and refining expert system knowledge bases. If you are
interested in more details, speak to George Drastal at the ML-92 conference or
send your vita along with one or two publication reprints to:
Human Resources
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
Please don't phone. We are, in any case, an equal opportunity employer.
------------------------------
Date: Fri, 19 Jun 92 17:42:35 EDT
From: Stephen Judd <judd@learning.siemens.COM>
Subject: CLNL workshop
LAST CALL FOR PAPERS
Third Annual Workshop on
Computational Learning Theory and `Natural' Learning Systems
August 27-29, Madison, Wisconsin
Siemens Corporate Research, MIT, and the University of Wisconsin-
Madison are sponsoring the third annual CLNL workshop to explore the
intersection of theoretical learning research and natural learning
systems. (Natural systems include those that have been successful
in a difficult engineering domain or those that represent natural
constraints arising from biological or psychological processes or
mechanisms.) The workshop will bring together a diverse set of
researchers from three relatively independent learning research areas:
Computational Learning Theory, AI/Machine Learning, and Connectionist
Learning. Invited speakers and participants will be encouraged to
examine general issues in learning systems which could provide
constraints for theory, while at the same time theoretical results will
be interpreted in the context of experiments with actual learning
systems.
Examples of experimental approaches include: Models or comparisons of
learning systems in classification problems (vision, speech, etc.);
Controls and Robotics, Natural language processing; Studies of
generalization; Representation effects on learning rate, noise
tolerance and concept or function complexity; Biological or
biologically inspired models of adaptation; Competitive processing or
synaptic growth and modification.
Relevant theoretical subjects include: The computational and sample
complexity of learning; Learning in the presence of noise; The effect
on learnability of prior knowledge, representational bias, or feature
construction; Learning protocol: learning sample distributions;
Efficient algorithms for learning particular classes of concepts or
functions; Comparison of analytical bounds with real-world
experiments.
Submission Procedure: Please submit 3 copies of a 100 word or less
abstract and a 2000 word or less summary of original research
indicating your preference for either experimental or theoretical
category. The DEADLINE for submission is JUNE 30, 1992.
Send abstracts and summaries to:
CLNL Workshop
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
(Or via email to clnl@learning.siemens.com)
--
ORGANIZING COMMITTEE
Andrew Barto, U Massachusetts
Andrew Barron, U Illinois
Stephen J. Hanson, Siemens Corp. Research
Michael Jordan, MIT
Stephen Judd, Siemens Corp. Research
Kumpati S. Narendra, Yale University
Tomaso Poggio, MIT
Larry Rendell, Beckman Institute
Ronald L. Rivest, MIT
Jude Shavlik, U Wisconsin
Paul Utgoff, U Massachusetts
WORKSHOP CO-CHAIRS
Thomas Petsche, Siemens Corp. Research
Jude Shavlik, U Wisconsin
Stephen Judd, Siemens Research
WORKSHOP SPONSORS
Siemens Corporate Research, Inc.
MIT Laboratory for Computer Science
University of Wisconsin, Dept. of Computer Science
------------------------------
Date: Thu, 18 Jun 92 14:19:28 EDT
From: gordon@aic.nrl.navy.MIL
To: ml@ics.uci.edu
Subject: workshop schedule: BIASES IN INDUCTIVE LEARNING
WORKSHOP ON "BIASES IN INDUCTIVE LEARNING"
After ML-92, on July 4, 1992
Aberdeen, Scotland
SCHEDULE
9:00-9:30 Introduction
Diana Gordon
9:30-10:30 INVITED TALK
Paul Utgoff
10:30-11:00 Coffee break
PROCEDURAL BIASES - Session chair: Devika Subramanian
11:00-11:30 Inductive Biases in a Reinforcement Learner
Helen Cobb
11:30-12:00 Is Consistency Harmful?
William Spears and Diana Gordon
12:00-12:30 A Baseline Taxonomy of Bias Selection Policies
Foster Provost
12:30-1:30 Lunch
PRIOR KNOWLEDGE AS A BIAS - Session chair: Foster Provost
1:30-2:00 Representing and Reasoning with Defaults for Learning Agents
Benjamin Grosof
2:00-2:30 Utilizing Prior Concepts for Learning
Piew Datta and Dennis Kibler
2:30-3:00 Case-based Meta Learning: Sustained Learning Supported by
a Dynamically Biased Version Space
Jacky Baltes
LANGUAGE BIASES - Session chair: William Spears
3:00-3:30 Computational Impact of Biases in Learning
Devika Subramanian and Scott Hunter
3:30-4:00 Coffee break
4:00-4:30 Biases in Syntactic Learning
Pieter Adriaans
4:30-5:00 Inventing Necessary Theoretical Terms to Overcome
Representation Bias
Charles Ling
5:00-5:30 A Comparative Study of Declarative and Dynamically Adjustable
Language Bias in Concept Learning
Hilde Ade and Maurice Bruynooghe
WORKSHOP CONCLUSION
5:30-5:40 Genetic Algorithms and Inductive Bias
William Spears
5:40-5:50 Final Remarks
Diana Gordon
**********************************************************************
For copies of the workshop proceedings, please contact:
Diana Gordon
Artificial Intelligence Center
Naval Research Laboratory, Code 5510
4555 Overlook Ave. S.W.
Washington, D.C. 20375-5000
U.S.A.
email: gordon@aic.nrl.navy.mil
phone: 202-767-2686
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End of ML-LIST 4.13 (Digest format)
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