| 000 | 03098nam a22006015i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-34106-9 | ||
| 003 | DE-He213 | ||
| 005 | 20140220083328.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 121001s2012 gw | s |||| 0|eng d | ||
| 020 |
_a9783642341069 _9978-3-642-34106-9 |
||
| 024 | 7 |
_a10.1007/978-3-642-34106-9 _2doi |
|
| 050 | 4 | _aQ334-342 | |
| 050 | 4 | _aTJ210.2-211.495 | |
| 072 | 7 |
_aUYQ _2bicssc |
|
| 072 | 7 |
_aTJFM1 _2bicssc |
|
| 072 | 7 |
_aCOM004000 _2bisacsh |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aBshouty, Nader H. _eeditor. |
|
| 245 | 1 | 0 |
_aAlgorithmic Learning Theory _h[electronic resource] : _b23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings / _cedited by Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann. |
| 264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2012. |
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| 300 |
_aXII, 381 p. 23 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v7568 |
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| 505 | 0 | _ainductive inference -- teaching and PAC learning -- statistical learning theory and classification -- relations between models and data -- bandit problems, online prediction of individual sequences.- other models of online learning. | |
| 520 | _aThis book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning. | ||
| 650 | 0 | _aComputer science. | |
| 650 | 0 | _aComputer software. | |
| 650 | 0 | _aLogic design. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aOptical pattern recognition. | |
| 650 | 1 | 4 | _aComputer Science. |
| 650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
| 650 | 2 | 4 | _aMathematical Logic and Formal Languages. |
| 650 | 2 | 4 | _aAlgorithm Analysis and Problem Complexity. |
| 650 | 2 | 4 | _aComputation by Abstract Devices. |
| 650 | 2 | 4 | _aLogics and Meanings of Programs. |
| 650 | 2 | 4 | _aPattern Recognition. |
| 700 | 1 |
_aStoltz, Gilles. _eeditor. |
|
| 700 | 1 |
_aVayatis, Nicolas. _eeditor. |
|
| 700 | 1 |
_aZeugmann, Thomas. _eeditor. |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783642341052 |
| 830 | 0 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v7568 |
|
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-34106-9 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-LNC | ||
| 999 |
_c103731 _d103731 |
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