Call for papers: Special issue Machine Learning

[thrun+@heaven.learning.cs.cmu.edu]

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

			      Call for papers

		Special Issue of the Machine Learning Journal

		                    on

			    Inductive Transfer

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

	        Lorien Pratt and Sebastian Thrun, Guest Editors

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

Many recent machine learning efforts are focusing on the question of how
to learn in an environment in which more than one task is performed by a
system.  As in human learning, related tasks can build on one another,
tasks that are learned simultaneously can cross-fertilize, and learning
can occur at multiple levels, where the learning process itself is a
learned skill.  Learning in such an environment can have a number of
benefits, including speedier learning of new tasks, a reduced number of
training examples for new tasks, and improved accuracy.  These benefits
are especially apparent in complex applied tasks, where the
combinatorics of learning are often otherwise prohibitive.

Current efforts in this quickly growing research area include
investigation of methods that facilitate learning multiple tasks
simultaneously, those that determine the degree to which two related
tasks can benefit from each other, and methods that extract and apply
abstract representations from a source task to a new, related, target
task.  The situation where the target task is a specialization of the
source task is an important special case.  The study of such methods has
broad application, including a natural fit to data mining systems, which
extract regularities from heterogeneous data sources under the guidance
of a human user, and can benefit from the additional bias afforded by
inductive transfer.

We solicit papers on inductive transfer and learning to learn for an
upcoming Special Issue of the Machine Learning Journal.  Please send six
(6) copies of your manuscript postmarked by July 15, 1996 to:

	Dr. Lorien Pratt
	MCS Dept.
	CSM
	Golden, CO  80401
	USA

One (1) additional copy should be mailed to:
	Karen Cullen
	Attn: Special Issue on Inductive Transfer
	MACHINE LEARNING Editorial Office
	Kluwer Academic Publishers
	101 Philip Drive
	Assinippi Park
	Norwell, MA  02061   USA

Manuscripts should be limited to at most 12000 words.  Please also note
that Machine Learning is now accepting submission of final copy in
electronic form.  Authors may want to adhere to the journal formatting
standards for paper submissions as well.  There is a latex style file
and related files available via anonymous ftp from ftp.std.com.  Look in
Kluwer/styles/journals for the files README, kbsfonts.sty, kbsjrnl.ins,
kbsjrnl.sty, kbssamp.tex, and kbstmpl.tex, or the file kbsstyles.tar.Z,
which contains them all.

Please see http://vita.mines.edu:3857/1s/lpratt/transfer.html for more
information on inductive transfer.

Papers will be quickly reviewed for a target publication date in the
first quarter of 1997.