CFP for special issue of Machine Learning Journal

[Padhraic J. Smyth]

                         CALL FOR PAPERS

              SPECIAL ISSUE OF THE MACHINE LEARNING JOURNAL
             ON LEARNING WITH PROBABILISTIC REPRESENTATIONS


Guest editors: 

Pat Langley (ISLE/Stanford University)
Gregory Provan (Rockwell Science Center/ISLE)
Padhraic Smyth (JPL/University of California, Irvine)


In recent years, probabilistic formalisms for representing knowledge
and inference techniques for using such knowledge have come to play
an important role in artificial intelligence. The further development
of algorithms for inducing such probabilistic knowledge from experience
has resulted in novel approaches to machine learning. 

To increase awareness of such probabilistic methods, including their
relation to each other and to other induction techniques, Machine 
Learning will publish a special issue on this topic. We encourage 
submission of papers that address all aspects of learning with
probabilistic representations, including but not limited to: Bayesian
networks, probabilistic concept hierarchies, naive Bayesian classifiers, 
mixture models, (hidden) Markov models, and stochastic context-free 
grammars. We consider any work on learning over representations with
explicit probabilistic semantics to fall within the scope of this issue.

Submissions should describe clearly the learning task, the representation
of data and learned knowledge, the performance element that uses this 
knowledge, and the induction algorithm itself. Moreover, we encourage 
authors to decompose their characterization of learning into the 
processes of (i) selecting a model (or family of models): what are
the properties of the model representation ? (ii) selecting a method
for evaluating the quality of a fitted model: given a particular
parametrization of the model what is the performance criterion by
which one can judge its quality ? and (iii) the algorithmic specification
of how to search over parameter and model space.  An ideal paper
will specify these three items clearly and relatively independently.

Papers should also evaluate the proposed methods using techniques
acknowledged in the machine learning literature, including but not
limited to: experimental studies of algorithm behavior on natural 
and synthetic data (but not the latter alone), theoretical analyses 
of algorithm behavior, ability to model psychological phenomena, 
and evidence of successful application in real-world contexts. We
especially encourage comparisons that clarify relations among
different probabilistic methods or to nonprobabilistic techniques.

Papers should meet the standard submission requirements given in the 
Machine Learning instructions to authors, including having length 
between 8,000 and 12,000 words. Hardcopies of each submission should 
be mailed to: 

Karen Cullen  (5 copies)		Pat Langley  (1 copy)
Kluwer Academic Publishers		Institute for the Study 
101 Philip Drive			  of Learning and Expertise
Assinippi Park				2164 Staunton Court
Norwell, MA 02061			Palo Alto, CA 94306

by the submission deadline, July 1, 1996. The review process will take 
into account the usual criteria, including clarity of presentation,
originality of the contribution, and quality of evaluation. We encourage 
potential authors to contact Pat Langley (langley at cs.stanford.edu), 
Gregory Provan (provan at jupiter.risc.rockwell.com), or Padhraic Smyth
(pjs at aig.jpl.nasa.gov) prior to submission if they have questions.