In this paper I advocate a new model for inductive learning. Called sequential induction, this model bridges classical fixed-sample learning techniques (which are efficient but ad hoc), and worst-case approaches (which provide strong statistical guarantees but are too inefficient for practical use). According to the sequential inductive model, learning is a sequence of decisions which are infor...

BCT (Binary Classification Tree) is a system that learns from examples and represents learned concepts as a binary polythetic decision tree. Polythetic trees differ from monothetic decision trees in that a logical combination of multiple (versus a single) attribute values may label each tree arc. Statistical evaluations are used to recursively partition the concept space in two and expand the t...

A number of heuristics have been developed which greatly reduce the search space a learning program must consider in its attempt to construct hypotheses about why a failure occurred. These heuristics have been implemented in the HANDICAPPER system [Salzberg 1983, Atkinson & Salzberg 1984], in which they significantly improved predictive ability while demonstrating a remarkable learning curve Th...

This position paper presents a framework for inductive machine learning which includes higher-order concepts and is su ciently general to include most of the extant (symbolic) inductive learning frameworks and systems.

Probabilistic inductive logic programming (PILP), sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed and they are being applied on applicat...