This paper reviews Machine Learning (ML), and extends and complements previous work
(Kocabas, 1991; Kalkanis and Conroy, 1991). Although this paper focuses on inductive …

This paper presents a new approach to identifying and eliminating mislabeled training
instances for supervised learning. The goal of this approach is to improve classification …

This paper firstly provides a re-appraisal of the development of techniques for inverting
deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a …

This paper summarizes the current state of the art in the topics of rst-order theory revision
and theory restructuring. The various tasks involved in rst-order theory re nement (revision …

A first-order framework for top-down induction of logical decision trees is introduced. The
expressivity of these trees is shown to be larger than that of the flat logic programs which are …

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to
induce a hypothesis (a logic program) that generalises given training examples and …

Abstract Inductive Logic Programming (ILP) is an area of Machine Learning which has now
reached its twentieth year. Using the analogy of a human biography this paper recalls the …

When communicating concepts, it is often convenient or even necessary to define a concept
approximately. A simple, although only approximately accurate concept definition may be …

Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive
and negative examples with respect to background knowledge expressed as logic …