By Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji Takimoto (eds.)
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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Extra info for Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings
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  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.
700, pp. 301–312, Springer-Verlag, Berlin, 1993. 18. M. Kearns L. Pitt, A polynomial-time algorithm for learning k –variable pattern languages from examples. in “Proc. Second Annual ACM Workshop on Computational Learning Theory” (pp. 57–71). San Mateo, CA: Morgan Kaufmann, 1989. 19. S. Lange and R. Wiehagen, Polynomial-time inference of arbitrary pattern languages. New Generation Computing 8 (1991), 361–370. 20. S. Lange and T. Zeugmann, Language learning in dependence on the space of hypotheses.
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.