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)
   kcullen@wkap.com
   
   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 imlm@cs.fit.edu.

General Inquiries:

Please address general inquiries to:

imlm@cs.fit.edu

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

Co-Editors:

Philip Chan, Florida Institute of Technology (pkc@cs.fit.edu)
Salvatore Stolfo, Columbia University (sal@cs.columbia.edu)
David Wolpert, NASA Ames Research Center (dhw@ptolemy.arc.nasa.gov)