Home Books


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.)

This ebook constitutes the refereed lawsuits of the 14th foreign convention on Algorithmic studying concept, ALT 2003, held in Sapporo, Japan in October 2003.

The 19 revised complete papers provided 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 knowledge extraction, studying with queries, studying with non-linear optimization, studying from random examples, and on-line prediction.

Show description

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

Similar computers books

Computer: A Very Short Introduction (Very Short Introductions)

What's the easy nature of the fashionable laptop? How does it paintings? How has it been attainable to squeeze a lot strength into more and more smaller machines? what is going to the subsequent generations of desktops glance like?

In this Very brief advent, Darrel Ince appears on the simple options at the back of all desktops, the adjustments in and software program that allowed pcs to develop into so small and normal, the demanding situations produced through the pc revolution--especially complete new modes of cybercrime and defense matters, the web and the appearance of "cloud computing," and the promise of entire new horizons establishing up with quantum computing and computing utilizing DNA

Ubiquitous Mobile Information and Collaboration Systems: Second CAiSE Workshop, UMICS 2004, Riga, Latvia, June 7-8, 2004, Revised Selected Papers

Over fresh years so much company procedures have replaced in quite a few dimensions (e. g. , ? exibility, interconnectivity, coordination type, autonomy) because of marketplace stipulations, organizational types, and utilization eventualities of data structures. often, inf- mationisrelocatedwithinageographicallydistributedsystemaccordingtorulesthatare purely seldom de?

iPhone for Work: Increasing Productivity for Busy Professionals (Books for Professionals by Professionals)

The purpose of this ebook is well for these skeptics company clients who simply jumped from the Blackberry at the iPhone educate, or they're pondering it. Being a complicated consumer myself, I most likely could not absolutely enjoy it, yet i'm definite no longer too tech savvy individual who is extra attracted to getting the trade provider to paintings, will love this publication.

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

Example text

Let j ∈ N be minimal such that ψi =n ψj . (* Note that, for all but finitely many n, the index j will be the minimal ψ-program of ψi . *) Return di (n) := dj (n). (* lim(di ) = dj , for j minimal with ψi = ψj . *) Finally, let di be given by the limit of the function di , in case a limit exists. Fix i ∈ N. Then there is a minimal j with ψi = ψj . By definition, the limit di of di exists and di = dj ∈ D. Moreover, as ψj ∈ Rdj , the function ψi is in Rdi . As ψ and (di )i∈N allow us to apply Theorem 7, the set D is UEx -complete.

Let D ∈ UEx . Assume ψ and (di )i∈N fulfil the conditions above. Let d be a recursive numbering corresponding to the limiting r. e. family (di )i∈N . By Property 2, Pψ is Ex -complete; thus, by Theorem 1, there exists a dense r. e. subclass C ⊆ Pψ . Let ψ be a one-one, recursive numbering with Pψ = C, in particular Pψ is dense. It remains to find a limiting r. e. family (di )i∈N of descriptions in D such that ψi ∈ Rdi for all i ∈ N. For that purpose define a corresponding numbering d . Given i, n ∈ N, define di (n) as follows.

L. Pitt, Inductive Inference, DFAs and Computational Complexity, in “Proc. 2nd Int. P. ), Lecture Notes in Artificial Intelligence, Vol. 397, pp. 18–44, Springer-Verlag, Berlin, 1989. 32. R. Reischuk and T. Zeugmann, Learning One- Variable Pattern Languages in Linear Average Time, in “Proc. 11th Annual Conference on Computational Learning Theory - COLT’98,” July 24th - 26th, Madison, pp. 198–208, ACM Press 1998. 33. R. Reischuk and T. Zeugmann, A Complete and Tight Average-Case Analysis of Learning Monomials, in “Proc.

Download PDF sample

Rated 4.61 of 5 – based on 47 votes