By Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji Takimoto (eds.)
This ebook constitutes the refereed lawsuits 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 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.
Read Online or Download Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings PDF
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Additional info for Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings
If δ, f are ﬁnite, Θ(δ, f ) = (γ, g), one can eﬀectively (in δ, f ) enumerate γ, g. This ﬁnally allows for the following deﬁnition of UEx -reducibility. Deﬁnition 10 Let D1 , D2 ∈ UEx . Fix a recursive meta-operator Θ and a recursive operator Ξ. D1 is UEx -reducible to D2 via Θ and Ξ, iﬀ for any d1 ∈ D1 , any f1 ∈ Rd1 , and any initial segment δ1 there are functions δ2 and f2 satisfying: 1. Θ(δ1 d∞ 1 , f1 ) = (δ2 , f2 ), 2. δ2 converges to some description d2 ∈ D2 such that f2 ∈ Rd2 , 3. if σ is admissible for f2 , then Ξ(σ) is admissible for f1 .
Assume D to be a class of admissible probability distributions over A+ such that α ≥ α∗ , β ≤ β∗ and E[Λ] ﬁnite for all distributions D ∈ D . Then (PAT , D) is stochastically ﬁnitely learnable with high conﬁdence from text. Proof. Let D ∈ D , and let δ ∈ (0, 1) be arbitrarily ﬁxed. Furthermore, let t = s1 , s2 , s3 , . . be any randomly generated text with respect to D for the target pattern language. The wanted learner M uses the LWA as a subroutine. Additionally, it has a counter for memorizing the number of examples already seen.
If the target pattern does not contain any variable then the LWA converges after having read the ﬁrst example. , the target pattern has to contain at least one variable. Our next theorem analyzes the complexity of the union operation. Theorem 7 (Rossmanith and Zeugmann ). The union operation can be computed in linear time. Furthermore, the following bound for the stage of convergence for every target pattern from Pat k can be shown. Theorem 8 (Rossmanith and Zeugmann ). 1 E[CONV ] = O · log1/β (k) for all k ≥ 2 .
Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings by Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji Takimoto (eds.)