KNOWLEDGE REUSE IN MULTIPLE CLASSIFIER SYSTEMS

We introduce a framework for the reuse of knowledge from previously tr ained classifiers to improve performance in a current and possibly rel ated classification task. The approach used is flexible in the type an d relevance of reused classifiers and is also scalable. Experiments on public domain data sets demonstrate the usefulness of this approach w hen one is faced with very few training examples or very noisy trainin g data. (C) 1997 Published by Elsevier Science B.V.