Algorithmic Learning Theory : 9th International Conference, Alt 98, Otzenhausen, Germany, October 8-10, 1998 : Proceedings

Richter, M. M. (EDT); Smith, Carl H. (EDT); Wiehagen, Rolf (EDT); Zeugmann, Thomas (EDT)

ISBN 10: 354065013X / ISBN 13: 9783540650133

Edité par Springer, 1998

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N° de réf. du vendeur 918021-n

Détails bibliographiques

• Titre: Algorithmic Learning Theory : 9th International Conference, Alt 98, Otzenhausen, Germany, October 8-10, 1998 : Proceedings
• Éditeur: Springer
• Date de parution: 1998
• Langue: anglais
• ISBN à 10 chiffres: 354065013X
• ISBN à 13 chiffres: 9783540650133
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT'98), held at the European education centre Europ]aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses.