ML-COLT '94 Workshop on Constructive Induction and Change of Representation

This is the home page for the Workshop on Constructive Induction and Change of Representation. The workshop was held on July 10, 1994 in conjunction with ML-COLT '94 (a joint conference of Machine Learning and Computational Learning Theory). All of the papers in the working notes are available from this page.

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Program Committee


For many years, researchers in machine learning have realized the importance of representation. In difficult learning problems (e.g. protein folding, word pronunciation and gene identification), considerable human effort is often required to identify useful terms of the representation language. In an effort to make learning more autonomous, researchers have investigated the problem of generating or modifying new representations automatically.

The past five years have seen a significant increase in the amount of work in this area. Some methods developed have been able to effect increases in classification accuracy. Others are able to derive features similar to those discovered previously by humans. Still other systems have demonstrated impressive performance improvement through the construction of new representations.

Many issues still remain in the field of constructive induction. We do not understand the range and limitations of current methods, or the kind of representation change that real-world domains may require. The objective of this workshop is to serve as a forum for the presentation of recent work, as well as a forum in which these issues can be discussed.

Papers and Abstracts

Click on a paper title to retrieve a Postscript copy of the entire paper. The papers are compressed; your client must be able to uncompress files.

Two of the papers in this workshop deal with molecular biology. A good introduction to this topic for machine learning people is Craven and Shavlik's paper Machine Learning Approaches to Gene Recognition.

Tom Fawcett (
Modified: 26-Jun-2001