the superarticulacy phenomenon in the context of software manufacture
The foundation of artificial intelligence---a sourcebook
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Numerical Initial Value Problems in Ordinary Differential Equations
Numerical Initial Value Problems in Ordinary Differential Equations
The Representation Race - Preprocessing for Handling Time Phenomena
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Predictive Performance of Weghted Relative Accuracy
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
GeLog - A System Combining Genetic Algorithm with Inductive Logic Programming
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Meta-analysis of Mutagenes Discovery
DS '01 Proceedings of the 4th International Conference on Discovery Science
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Machine Learning algorithms are being increasingly used for knowledge discovery tasks. Approaches can be broadly divided by distinguishing discovery of procedural from that of declarative knowledge. Client requirements determine which of these is appropriate. This paper discusses an experimental application of machine learning in an area related to drug design. The bottleneck here is in finding appropriate constraints to reduce the large number of candidate molecules to be synthesised and tested. Such constraints can be viewed as declarative specifications of the structuralel ements necessary for high medicinal activity and low toxicity. The first-order representation used within Inductive Logic Programming (ILP) provides an appropriate description language for such constraints. Within this application area knowledge accreditation requires not only a demonstration of predictive accuracy but also, and crucially, a certification of novel insight into the structuralc hemistry. This paper describes an experiment in which the ILP system Progolw as used to obtain structural constraints associated with mutagenicity of molecules. In doing so Progol found a new indicator of mutagenicity within a subset of previously published data. This subset was already known not to be amenable to statistical regression, though its complement was adequately explained by a linear model. According to the combined accuracy/explanation criterion provided in this paper, on both subsets comparative trials show that Progol's structurally-oriented hypotheses are preferable to those of other machine learning algorithms.