CFP: AAAI-96 Workshop on Integrating Multiple Learned Models

[IMLM Workshop (pkc)]

		    CALL FOR PAPERS/PARTICIPATION


		 INTEGRATING MULTIPLE LEARNED MODELS
	FOR IMPROVING AND SCALING MACHINE LEARNING ALGORITHMS

	       to be held in conjunction with AAAI 1996
			   Portland, Oregon
			     August 1996


Most modern machine learning research uses a single model or learning
algorithm at a time, or at most selects one model from a set of
candidate models. Recently however, there has been considerable
interest in techniques that integrate the collective predictions of a
set of models in some principled fashion.  With such techniques often
the predictive accuracy and/or the training efficiency of the overall
system can be improved, since one can "mix and match" among the
relative strengths of the models being combined.

The goal of this workshop is to gather researchers actively working in
the area of integrating multiple learned models, to exchange ideas and
foster collaborations and new research directions.  In particular, we
seek to bring together researchers interested in this topic from the
fields of Machine Learning, Knowledge Discovery in Databases, and
Statistics.

Any aspect of integrating multiple models is appropriate for the
workshop. However we intend the focus of the workshop to be improving
prediction accuracies, and improving training performance in the
context of large training databases.

More precisely, submissions are sought in, but not limited to, the
following topics:

1) Techniques that generate and/or integrate multiple learned
   models. In particular, techniques that do so by:

	* using different training data distributions
		(in particular by training over different partitions
		of the data)
	* using different output classification schemes
		(for example using output codes)
       	* using different hyperparameters or training heuristics
		(primarily as a tool for generating multiple models)

	2) Systems and architectures to implement such strategies. In particular:

        * parallel and distributed multiple learning systems
        * multi-agent learning over inherently distributed data

A paper need not be submitted to participate in the workshop, but
space may be limited so contact the organizers as early as possible if
you wish to participate.

The workshop format is planned to encompass a full day of half hour
presentations with discussion periods, ending with a brief period for
summary and discussion of future activities.  Notes or proceedings for
the workshop may be provided, depending on the submissions received.


Submission requirements:

i) A short paper of not more than 2000 words detailing recent research
results must be received by March 18, 1996.

ii) The paper should include an abstract of not more than 150 words,
and a list of keywords.  Please include the name(s), email
address(es), address(es), and phone number(s) of the author(s) on the
first page.  The first author will be the primary contact unless
otherwise stated.

iii) Electronic submissions in postscript or ASCII via email are
preferred.  Three printed copies (preferrably double-sided) of your
submission are also accepted.

iv) Please also send the title, name(s) and email address(es) of the
author(s), abstract, and keywords in ASCII via email.



Submission address:

	imlm at cs.fit.edu

	Philip Chan
	IMLM Workshop
	Computer Science
	Florida Institute of Technology
	150 W. University Blvd.
        Melbourne, FL 32901-6988
	407-768-8000 x7280 (x8062)
	407-984-8461 (fax)


Important Dates:

	Paper submission deadline:	March 18, 1996
	Notification of acceptance:	April 15, 1996
	Final copy:			May 13, 1996


Chairs:

        Salvatore Stolfo, Columbia University		sal at cs.columbia.edu
        David Wolpert, Santa Fe Institute		dhw at santafe.edu
	Philip Chan, Florida Institute of Technology	pkc at cs.fit.edu


General Inquiries:

Please address general inquiries to one of the co-chairs or send them
to:

	imlm at cs.fit.edu

Up-to-date workshop information is maintained on WWW at:

	http://cs.fit.edu/~imlm/ or
	http://www.cs.fit.edu/~imlm/