Algorithmic learning theory

This volume contains the papers presented at the 14th Annual Conference on Algorithmic Learning Theory (ALT 2003), which was held in Sapporo (Japan) duringOctober17–19,2003. Themainobjectiveoftheconferencewastoprovide an interdisciplinary forum for discussing the theoretical foundations of machine learning as well as their relevance to practical applications. The conference was co-locatedwiththe6thInternationalConferenceonDiscoveryScience(DS2003). The volume includes 19 technical contributions that were selected by the program committee from 37 submissions. It also contains the ALT 2003 invited talks presented by Naftali Tishby (Hebrew University, Israel) on “E?cient Data Representations that Preserve Information,” by Thomas Zeugmann (University of Lub ¨ eck, Germany) on “Can Learning in the Limit be Done E?ciently?”, and by Genshiro Kitagawa (Institute of Statistical Mathematics, Japan) on “S- nal Extraction and Knowledge Discovery Based on Statistical Modeling” (joint invited talk with DS 2003). Furthermore, this volume includes abstracts of the invitedtalksforDS2003presentedbyThomasEiter(ViennaUniversityofTe- nology, Austria) on “Abduction and the Dualization Problem” and by Akihiko Takano (National Institute of Informatics, Japan) on “Association Computation for Information Access. ” The complete versions of these papers were published in the DS 2003 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2843). ALT has been awarding theE. MarkGoldAward for the most outstanding paper by a student author since 1999. This year the award was given to Sandra Zilles for her paper “Intrinsic Complexity of Uniform Learning. ” This conference was the 14th in a series of annual conferences established in 1990. ContinuationoftheALTseriesissupervisedbyitssteeringcommittee,c- sisting of: Thomas Zeugmann (Univ.