000 | 03590nam a22006255i 4500 | ||
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007 | cr nn 008mamaa | ||
008 | 121227s2004 gw | s |||| 0|eng d | ||
020 |
_a9783540286509 _9978-3-540-28650-9 |
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024 | 7 |
_a10.1007/b100712 _2doi |
|
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050 | 4 | _aTA347.A78 | |
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_aAdvanced Lectures on Machine Learning _h[electronic resource] : _bML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures / _cedited by Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch. |
250 | _a1st ed. 2004. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2004. |
|
300 |
_aX, 246 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v3176 |
|
505 | 0 | _aAn Introduction to Pattern Classification -- Some Notes on Applied Mathematics for Machine Learning -- Bayesian Inference: An Introduction to Principles and Practice in Machine Learning -- Gaussian Processes in Machine Learning -- Unsupervised Learning -- Monte Carlo Methods for Absolute Beginners -- Stochastic Learning -- to Statistical Learning Theory -- Concentration Inequalities. | |
520 | _aMachine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputer science. | |
650 | 0 | _aAlgorithms. | |
650 | 0 | _aPattern recognition systems. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aComputer Science. |
650 | 2 | 4 | _aAlgorithms. |
650 | 2 | 4 | _aTheory of Computation. |
650 | 2 | 4 | _aAutomated Pattern Recognition. |
700 | 1 |
_aBousquet, Olivier. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aLuxburg, Ulrike von. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aRätsch, Gunnar. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540231226 |
776 | 0 | 8 |
_iPrinted edition: _z9783662185483 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v3176 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/b100712 |
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