Subject: CFP: MLJ special issue on IMLM
From: [email-masked] (Philip Chan)
Date: 10 May 1997 15:39:40 -0400
Newsgroups: comp.ai,comp.ai.genetic,comp.ai.neural-nets

CALL FOR PAPERS
 
                        Machine Learning Journal
                            Special Issue on

                  Integrating Multiple Learned Models
         for Improving and Scaling Machine Learning Algorithms

 Most modern Machine Learning, Statistics and KDD techniques use a
 single model or learning algorithm at a time, or at most select 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.
 
 Any aspect of integrating multiple models is appropriate for the
 special issue.  However we intend the focus of the special issue to be
 on the issues of improving prediction accuracy and improving training
 efficiency in the context of large databases.

 Submissions are sought in, but not limited to, the following topics:
 
 1) Techniques that generate and/or integrate multiple learned
    models.   Examples are schemes that generate and combine
    models by
 
         * using different training data distributions
                 (in particular by training over different partitions
                 of the data)
         * using different sampling techniques to generate different
                 partitions
         * 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.
    For example,
 
         * parallel and distributed multiple learning systems
         * multi-agent learning over inherently distributed data
 
 3) Techniques that analyze the integration of multiple learned models for
 
         * selecting/pruning models
         * estimating the overall accuracy
         * comparing different integration methods
         * tradeoff of accuracy and simplicity/comprehensibility

 Schedule:
 
         October 1: Deadline for submissions
         December 15: Deadline for getting decisions back to authors
         March 15: Deadline for authors to submit final versions
         August 1998: Publication

 Submission Guidelines:
 
 1) Manuscripts should conform to the formatting instructions in:
 
         http://www.cs.orst.edu/~tgd/mlj/info-for-authors.html
 
    The first author will be the primary contact unless otherwise stated.
 
 2) Authors should send 5 copies of the manuscript to:
 
         Karen Cullen
         Machine Learning Editorial Office
         Attn: Special Issue on IMLM
         Kluwer Academic Press
         101 Philip Drive
         Assinippi Park
         Norwell, MA 02061
         617-871-6300
         617-871-6528 (fax)
         [email-masked]
 
    and one copy to:
 
         Philip Chan
         MLJ Special Issue on IMLM
         Computer Science
         Florida Institute of Technology
         150 W. University Blvd.
         Melbourne, FL 32901
         407-768-8000 x7280 (x8062) (407-674-7280/8062 after 6/1/97)
         407-984-8461 (fax)
 
 3) Please also send an ASCII title page (title, authors, email, abstract,
    and keywords) and a postscript version of the manuscript to
    [email-masked].

 General Inquiries:
 
   Please address general inquiries to:
 
       [email-masked]
 
   Up-to-date information is maintained on WWW at:
 
       http://www.cs.fit.edu/~imlm/

 Co-Editors:
 
         Philip Chan, Florida Institute of Technology    [email-masked]
         Salvatore Stolfo, Columbia University           [email-masked]
         David Wolpert, IBM Almaden Research Center      [email-masked]