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Algorithmic Learning Theory - 10th International Conference, ALT '99 Tokyo, Japan, December 6-8, 1999 Proceedings (Paperback, 1999 ed.)
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Algorithmic Learning Theory - 10th International Conference, ALT '99 Tokyo, Japan, December 6-8, 1999 Proceedings (Paperback, 1999 ed.)
Series: Lecture Notes in Computer Science, 1720
Expected to ship within 10 - 15 working days
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ThisvolumecontainsallthepaperspresentedattheInternationalConferenceon
Algorithmic Learning Theory 1999 (ALT'99), held at Waseda
University Int- nationalConferenceCenter, Tokyo, Japan, December
6?8,1999.Theconference was sponsored by the Japanese Society for
Arti cial Intelligence (JSAI). In response to the call for papers,
51 papers on all aspects of algorithmic learning theory and related
areas were submitted, of which 26 papers were - lected for
presentation by the program committee based on their originality,
quality, and relevance to the theory of machine learning. In
addition to these regular papers, this volume contains three papers
of invited lectures presented
byKatharinaMorikoftheUniversityofDortmund,
RobertE.SchapireofAT&T Labs, Shannon Lab., and Kenji Yamanishi
of NEC, C&C Media Research Lab. ALT'99 is not just one of the
ALT conference series, but this conference marks the tenth
anniversary in the series that was launched in Tokyo, in Oc- ber
1990, for the discussion of research topics on all areas related to
algorithmic learning theory. The ALT series was renamedlast year
from\ALT workshop"to \ALT conference,"expressing its wider goalof
providing an ideal forum to bring together researchers from both
theoretical and practical learning communities, producing novel
concepts and criteria that would bene t both. This movement
wasre?ectedinthepaperspresentedatALT'99,
wheretherewereseveralpapers motivated by application oriented
problems such as noise, data precision, etc. Furthermore, ALT'99
benet ed from being held jointly with the 2nd Inter- tional
Conference on Discovery Science (DS'99), the conference for
discussing, among other things, more applied aspects of machine
learning. Also, we could celebrate the tenth anniversary of the ALT
series with researchers from both theoretical and practical
communities.
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