In this paper we are concerned with the problem of acquiring knowledge by integration. Our
aim is to construct an integrated knowledge base from several separate sources. The need …

The paper begins with the discussion on why we should be concerned with machine
learning in the context of distributed AI. The rest of the paper is dedicated to various …

The information retrieval community is becoming increasingly interested in machine learning
techniques, of which text categorization is an application. This paper describes how we have …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …

An induction-based method for retrieving similar cases and/or easily adaptable cases is
presented in a 3-steps process: first, a rule set is learned from a data set; second, a …

This paper describes how a competitive tree learning algorithm can be derived from rst
principles. The algorithm approximates the Bayesian decision theoretic solution to the …

Since researchers in each of the many various subfields of machine learning are often
unaware of related work done within the other subfields, this book brings together many of …

This paper introduces a new concept learning system. Its main features are presented and
discussed. The controlled use of redundancy is one of the main characteristics of the …

This extended abstract summarises our current research which spans the fields of
knowledge discovery and software agents. Knowledge discovery (or data-mining) is …

This paper shows, by discussing a number of Machine Learning (ML) applications, that the
existing ML techniques can be effectively applied in knowledge acquisition for expert …