Klaus P. Jantke

Nada Lavrač

Ljupco Todorovski

This book constitutes the refereed proceedings of the 9th International Conference on Discovery Science, DS 2006, held in Barcelona, Spain in October 2006, co-located with the 17th International Conference on Algorithmic Learning Theory, ALT 2006. The 23 revised long papers and the 18 revised regular papers presented together with five invited papers were carefully reviewed and selected from 87 submissions.

9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings

Discovery Science

Johannes Fürnkranz

Dragan Gamberger

Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

Foundations of Rule Learning

Stefan Wrobel

This volume constitutes the proceedings of the Eighth European Conference on Machine Learning ECML-95, held in Heraclion, Crete in April 1995. Besides four invited papers the volume presents revised versions of 14 long papers and 26 short papers selected from a total of 104 submissions. The papers address all current aspects in the area of machine learning; also logic programming, planning, reasoning, and algorithmic issues are touched upon.

8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25 - 27, 1995. Proceedings

Machine Learning: ECML-95

Filip Železný

The 18th International Conference on Inductive Logic Programming was held in Prague, September 10–12, 2008. ILP returned to Prague after 11 years, and it is tempting to look at how the topics of interest have evolved during that time. The ILP community clearly continues to cherish its beloved ?rst-order logic representation framework. This is legitimate, as the work presented at ILP 2008 demonstrated that there is still room for both extending established ILP approaches (such as inverse entailment) and exploring novel logic induction frameworks (such as brave induction). Besides the topics lending ILP research its unique focus, we were glad to see in this year’s proceedings a good n- ber of papers contributing to areas such as statistical relational learning, graph mining, or the semantic web. To help open ILP to more mainstream research areas, the conference featured three excellent invited talks from the domains of the semantic web (Frank van Harmelen), bioinformatics (Mark Craven) and cognitive sciences (Josh Tenenbaum). We deliberately looked for speakers who are not directly involved in ILP research. We further invited a tutorial on stat- tical relational learning (Kristian Kersting) to meet the strong demand to have the topic presented from the ILP perspective. Lastly, Stefano Bertolo from the European Commission was invited to give a talk on the ideal niches for ILP in the current EU-supported research on intelligent content and semantics.

18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008, Proceedings

Inductive Logic Programming

Propositionalization and Embeddings

Representation Learning

Nada Lavrač

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