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 booklet constitutes the refereed complaints of the 14th foreign convention on Algorithmic studying conception, 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 conscientiously reviewed and chosen from 37 submissions. The papers are equipped in topical sections on inductive inference, studying and knowledge extraction, studying with queries, studying with non-linear optimization, studying from random examples, and on-line prediction.

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

Sample text

E. family of descriptions in D. It remains to verify Property 2. For that purpose fix i, n ∈ N. By definition, if i encodes (x, y), we have Θ(αy g(x)∞ , s) = (δi , ψi ). The properties of Θ yield some m ∈ N such that Θ(αy g(x)m , s) = (δi , α ) for some δi and α with δi ⊆ δi and ψi [n] ⊆ α ⊆ ψi . Now choose any x ∈ N such that x = x. Moreover, there is some y ∈ N such that αy = αy g(x)m . If j encodes (x , y ), this yields Θ(αy g(x)m g(x )∞ , s) = (δj , ψj ), where α ⊆ ψj . In particular ψj =n ψi .

Clearly, the learner described above is finite. Let L be the target language and let π ∈ Pat k be the unique pattern such that L = L(π) . It remains to argue that L(π) = L(τ ) with probability at least 1 − δ . First, the bound in (A) is an upper bound for the expected number of examples needed for convergence by the LWA that has been established in Theorem 8 (via the reformulation using E[MC ] given above). On the one hand, this follows from our assumptions about the allowed α and β as well as from the fact that |τ | ≥ |π| for every hypothesis output.

Information and Control 69 (1986), 12–40. 11. T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger and T. Zeugmann, Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries, Theoretical Computer Science 261, No. 1–2, 2001, 119–156. 12. M. Gold, Language identification in the limit, Information and Control 10 (1967), 447–474. 13. A. J. E. Schapire, Exact identification of circuits using fixed points of amplification functions. SIAM Journal of Computing 22, 1993, 705–726.

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