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  Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations

Case, J., & Kötzing, T. (2010). Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations. In M. Hutter, F. Stephan, V. Vovk, & T. Zeugmann (Eds.), Algorithmic Learning Theory (pp. 285-299). Berlin: Springer. doi:10.1007/978-3-642-16108-7_24.

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 Creators:
Case, John1, Author
Kötzing, Timo2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Algorithms and Complexity, MPI for Informatics, Max Planck Society, ou_24019              

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 Abstract: A U-shape occurs when a learner first learns, then unlearns, and, finally, relearns, some target concept. Within the framework of Inductive Inference, previous results have shown, for example, that U-shapes are unnecessary for explanatory learning, but are necessary for behaviorally correct learning. This paper solves the following two problems regarding non-U-shaped learning posed in the prior literature. First, it is shown that there are classes learnable with three memory states that are not learnable non-U-shapedly with any finite number of memory states. This result is surprising, as for learning with one or two memory states, U-shapes are known to be unnecessary. Secondly, it is shown that there is a class learnable memorylessly with a single feedback query such that this class is not learnable non-U-shapedly memorylessly with any finite number of feedback queries. This result is complemented by the result that any class of \emph{infinite} languages learnable memorylessly with finitely many feedback queries is so learnable without U-shapes -- in fact, all classes of infinite languages learnable with \emph{complete} memory are learnable memorylessly with finitely many feedback queries and without U-shapes. On the other hand, we show that there is a class of infinite languages learnable memorylessly with a single feedback query, which is \emph{not} learnable with\emph{out} U-shapes by any particular bounded number of feedback queries.

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Language(s): eng - English
 Dates: 20102010
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 536694
DOI: 10.1007/978-3-642-16108-7_24
Other: Local-ID: C1256428004B93B8-62C151E58D79F70FC12577F80053535A-Koetzing2010NUMemLimited
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Title: 21st International Conference on Algorithmic Learning Theory
Place of Event: Canberra, Australia
Start-/End Date: 2010-12-13 - 2010-12-13

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Title: Algorithmic Learning Theory
Source Genre: Proceedings
 Creator(s):
Hutter, Marcus1, Editor
Stephan, Frank1, Editor
Vovk, Vladimir1, Editor
Zeugmann, Thomas1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Berlin : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 285 - 299 Identifier: ISBN: 978-3-642-16107-0

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Title: Lecture Notes in Artificial Intelligence
  Abbreviation : LNAI
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 6331 Sequence Number: - Start / End Page: - Identifier: -