Home Computers • Download Algorithmic Learning Theory: 14th International Conference, by Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji PDF

Download Algorithmic Learning Theory: 14th International Conference, by Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji PDF

By Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji Takimoto (eds.)

ISBN-10: 3540202919

ISBN-13: 9783540202912

This publication constitutes the refereed complaints of the 14th overseas convention on Algorithmic studying thought, ALT 2003, held in Sapporo, Japan in October 2003.

The 19 revised complete papers awarded including 2 invited papers and abstracts of three invited talks have been rigorously reviewed and chosen from 37 submissions. The papers are prepared in topical sections on inductive inference, studying and data extraction, studying with queries, studying with non-linear optimization, studying from random examples, and on-line prediction.

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Read or Download Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings PDF

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Extra info for Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings

Sample text

However, much more effort seems necessary to design a stochastic finite learner for PAT (k) . Additionally, we have applied our techniques to design a stochastic finite learner for the class of all concepts describable by a monomial which is based on Haussler’s [14] Wholist algorithm. Here we have assumed the examples to be binomially distributed. The sample size of our stochastic finite learner is mainly bounded by log(1/δ) log n , where δ is again the confidence parameter and n is the dimension of the underlying Boolean learning domain.

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R. Wiehagen. Limes-Erkennung rekursiver Funktionen durch spezielle Strategien. Journal of Information Processing and Cybernetics (EIK) 12, 1976, 93–99. 48. R. Wiehagen and T. Zeugmann, Ignoring Data may be the only Way to Learn Efficiently, Journal of Experimental and Theoretical Artificial Intelligence 6 (1994), 131–144. 49. K. Wright, Identification of unions of languages drawn from an identifiable class, in “Proceedings of the 2nd Workshop on Computational Learning Theory,” (R. Rivest, D. Haussler, and M.

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