Set-Driven and Rearrangement-Independent Learning of Recursive Languages.

Autor: Lange, S., Zeugmann, T.

Publikováno v: Mathematical Systems Theory. 1996, Vol. 29 Issue 6, p599. 36p. 1 Chart.

Regular inference as vertex coloring

Autor: Costa Florêncio, C., Verwer, S., Bshouty, N.H., Stolz, G., Vayatis, N., Zeugmann, T.

Publikováno v: Algorithmic Learning Theory: 23rd International Conference, ALT 2012, Lyon, France, October 29-31 2012: proceedings, 81-95
STARTPAGE=81;ENDPAGE=95;TITLE=Algorithmic Learning Theory
Lecture Notes in Computer Science ISBN: 9783642341052
ALT
Bshouty, N.; Stoltz, G. (ed.), Algorithmic Learning Theory : 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings, pp. 81-95
Lecture Notes in Computer Science ; 7568, 81-95. Berlin : Springer
STARTPAGE=81;ENDPAGE=95;TITLE=Lecture Notes in Computer Science ; 7568

This paper is concerned with the problem of supervised learning of deterministic finite state automata, in the technical sense of identification in the limit from complete data, by finding a minimal DFA consistent with the data (regular inference).We

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86d68c2e5f9f30e20c8daf2cb2e8c880
https://dare.uva.nl/personal/pure/en/publications/regular-inference-as-vertex-coloring(4c0ae5a3-3107-427e-893e-a1925b05e49a).html

Computable Bayesian Compression for Uniformly Discretizable Statistical Models

Autor: Debowski, Lukasz Jerzy, Gavalda, R., Lugosi, G., Zeugmann, T.

Supplementing Vovk and V'yugin's `if' statement, we show that Bayesian compression provides the best enumerable compression for parameter-typical data if and only if the parameter is Martin-L\"of random with respect to the prior. The result is derive

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::22ed39b530a958cd0e904ed62b55c734
https://ir.cwi.nl/pub/14595

Recent developments in pattern mining

Autor: Calders, T.G.K., Bshouty, N.H., Stoltz, G., Vayatis, N., Zeugmann, T.

Publikováno v: Discovery Science ISBN: 9783642334917
Algorithmic Learning Theory (23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings), 34-34
STARTPAGE=34;ENDPAGE=34;TITLE=Algorithmic Learning Theory (23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings)
Lecture Notes in Computer Science ISBN: 9783642341052
Discovery Science
Discovery Science (15th International Conference, DS 2012, Lyon, France, October 29-31, 2012. Proceedings), 2-2
STARTPAGE=2;ENDPAGE=2;TITLE=Discovery Science (15th International Conference, DS 2012, Lyon, France, October 29-31, 2012. Proceedings)

Pattern Mining is one of the most researched topics in the data mining community. Literally hundreds of algorithms for efficiently enumerating all frequent itemsets have been proposed. These exhaustive algorithms, however, all suffer from the pattern

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6abcf2fc281d01789219c8699919c490
https://doi.org/10.1007/978-3-642-34106-9_5

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