AAAI-96 Workshop on Integrating Multiple Learned Models

Integrating Multiple Learned Models
for Improving and Scaling Machine Learning Algorithms
to be held in conjunction with AAAI-96
(collocated with KDD-96, UAI-96, and IAAI-96)
Portland, Oregon
August 4-5th, 1996

CALL FOR PAPERS/PARTICIPATION

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 efficiency in the context of large training databases.

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

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)
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:

A short paper of not more than 2000 words detailing recent research results must be received by March 18, 1996.
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.
Electronic submissions in postscript or ASCII via email are preferred. Three printed copies (preferrably double-sided) of your submission are also accepted.
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@cs.fit.edu

IMLM Workshop

Computer Science

Florida Institute of Technology

Important Dates:

Chairs:

Philip Chan

Florida Institute of Technology

pkc@cs.fit.edu

Salvatore Stolfo

Columbia University