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Algorithmic Learning Theory 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings /
This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were caref...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
|
| Edición: | 1st ed. 2015. |
| Colección: | Lecture Notes in Artificial Intelligence,
9355 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Algorithmic Learning Theory |h [electronic resource] : |b 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings / |c edited by Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a XVII, 395 p. 26 illus. in color. |b online resource. | ||
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| 490 | 1 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 9355 | |
| 505 | 0 | |a Inductive inference -- Learning from queries, teaching complexity -- Computational learning theory and algorithms -- Statistical learning theory and sample complexity -- Online learning -- Stochastic optimization -- Kolmogorov complexity, algorithmic information theory. | |
| 520 | |a This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer science. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Theory of Computation. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
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| 700 | 1 | |a GENTILE, CLAUDIO. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Zilles, Sandra. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 830 | 0 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 9355 | |
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