Algorithmic Learning Theory : 11th International Conference, Alt 2000 Sydney, Australia, December 11-13, 2000, Proceedings

Arimura, Hiroki (EDT); Jain, Sanjay (EDT); Sharma, Arun K. (EDT)

ISBN 10: 3540412379 / ISBN 13: 9783540412373

Edité par Springer, 2000

A propos de cet article

N° de réf. du vendeur 980590-n

Détails bibliographiques

• Titre: Algorithmic Learning Theory : 11th International Conference, Alt 2000 Sydney, Australia, December 11-13, 2000, Proceedings
• Éditeur: Springer
• Date de parution: 2000
• Langue: anglais
• ISBN à 10 chiffres: 3540412379
• ISBN à 13 chiffres: 9783540412373
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

This volume contains all the papers presented at the Eleventh International C- ference on Algorithmic Learning Theory (ALT 2000) held at Coogee Holiday Inn, Sydney, Australia,11-13 December 2000. The conference was sponsored by the School of Computer Science and Engineering, University of New South Wales, and supported by the IFIP Working Group 1.4 on Computational Learning T- ory and the Computer Science Association (CSA) of Australia. In response to the call for papers 39 submissions were received on all aspects of algorithmic learning theory. Out of these 22 papers were accepted for p- sentation by the program committee. In addition, there were three invited talks by William Cohen (Whizbang Labs), Tom Dietterich (Oregon State Univeristy), and Osamu Watanabe (Tokyo Institute of Technology). This year's conference is the last in the millenium and eleventh overall in the ALT series. The ?rst ALT workshop was held in Tokyo in 1990. It was merged with the workshop on Analogical and Inductive Inference in 1994. The conf- ence focuses on all areas related to algorithmic learning theory, including (but not limited to) the design and analysis of learning algorithms, the theory of machine learning, computational logic of/for machine discovery, inductive inf- ence, learning via queries, new learning models, scienti?c discovery, learning by analogy, arti?cial and biological neural networks, pattern recognition, statistical learning, Bayesian/MDL estimation, inductive logic programming, data m- ing and knowledge discovery, and application of learning to biological sequence analysis. In the current conference there were papers from a variety of the above areas, refelecting both the theoretical as well as practical aspects of learning.