Heikki Mannila – författare

The interconnected ideas of inductive databases and constraint-based mining are appealing and have the potential to radically change the theory and practice of data mining and knowledge discovery. This book reports on the results of the European IST project "cInQ" (consortium on knowledge discovery by Inductive Queries) and its final workshop entitled Constraint-Based Mining and Inductive Databases organized in Hinterzarten, Germany in March 2004.

This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002.The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.

We are pleased to present the proceedings of the 13th European Conference on Machine Learning (LNAI 2430) and the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 2431). These two c- ferences were colocated in Helsinki, Finland during August 19-23, 2002. ECML and PKDD were held together for the second year in a row, following the success of the colocation in Freiburg in 2001. Machine learning and knowledge discovery are two highly related ?elds and ECML/PKDD is a unique forum to foster their collaboration. The bene?t of colocation to both the machine learning and data mining communities is most clearly displayed in the common workshop, tutorial, and invited speaker program. Altogether six workshops and six tutorials were or- nized on Monday and Tuesday. As invited speakers we had the pleasure to have Erkki Oja (Helsinki Univ. of Technology), Dan Roth (Univ. of Illinois, Urbana- Champaign), Bernhard Scholkopf (Max Planck Inst. for Biological Cybernetics, Tubingen), and Padhraic Smyth (Univ. of California, Irvine). The main events ran from Tuesday until Friday, comprising 41 ECML te- nical papers and 39 PKDD papers.In total, 218 manuscripts were submitted to these two conferences: 95 to ECML, 70 to PKDD, and 53 as joint submissions. All papers were assigned at least three reviewers from our international program committees. Out of the 80 accepted papers 31 were ?rst accepted conditi- ally; the revised manuscripts were accepted only after the conditions set by the reviewers had been met.

For many years Esko Ukkonen has played a major role in the advancement of computer science in Finland. He was the key person in the development of the school of algorithmic research and has contributed considerably to post-graduate education in his country.Esko Ukkonen has over the years worked within many areas of computer science, including numerical methods, complexity theory, theoretical aspects of compiler construction, and logic programming. However, the main focus of his research has been on algorithms and their applications. This Festschrift volume, published to honor Esko Ukkonen on his 60th birthday, includes 18 refereed contributions by his former PhD students and colleagues, with whom he has cooperated closely during the course of his career. The Festschrift was presented to Esko during a festive symposium organized at the University of Helsinki to celebrate his birthday. The essays primarily present research on computational pattern matching and string algorithms, two areas that have benefited significantly from the work of Esko Ukonen.

This book constitutes the refereed proceedings of the 14th International Conference on Discovery Science, DS 2011, held in Espoo, Finland, in October 2011 - co-located with ALT 2011, the 22nd International Conference on Algorithmic Learning Theory. The 24 revised full papers presented together with 5 invited lectures were carefully revised and selected from 56 submissions. The papers cover a wide range including the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery.