000 | 03502nam a22005895i 4500 | ||
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001 | 978-3-030-05318-5 | ||
003 | DE-He213 | ||
005 | 20240423124950.0 | ||
007 | cr nn 008mamaa | ||
008 | 190517s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030053185 _9978-3-030-05318-5 |
||
024 | 7 |
_a10.1007/978-3-030-05318-5 _2doi |
|
050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aCOM004000 _2bisacsh |
|
072 | 7 |
_aUYQ _2thema |
|
082 | 0 | 4 |
_a006.3 _223 |
245 | 1 | 0 |
_aAutomated Machine Learning _h[electronic resource] : _bMethods, Systems, Challenges / _cedited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
|
300 |
_aXIV, 219 p. 54 illus., 45 illus. in color. _bonline resource. |
||
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 |
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490 | 1 |
_aThe Springer Series on Challenges in Machine Learning, _x2520-1328 |
|
505 | 0 | _a1 Hyperparameter Optimization -- 2 Meta-Learning -- 3 Neural Architecture Search -- 4 Auto-WEKA -- 5 Hyperopt-Sklearn -- 6 Auto-sklearn -- 7 Towards Automatically-Tuned Deep Neural Networks -- 8 TPOT -- 9 The Automatic Statistician -- 10 AutoML Challenges. | |
506 | 0 | _aOpen Access | |
520 | _aThis open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputer vision. | |
650 | 0 | _aPattern recognition systems. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aComputer Vision. |
650 | 2 | 4 | _aAutomated Pattern Recognition. |
700 | 1 |
_aHutter, Frank. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aKotthoff, Lars. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aVanschoren, Joaquin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030053178 |
776 | 0 | 8 |
_iPrinted edition: _z9783030053192 |
830 | 0 |
_aThe Springer Series on Challenges in Machine Learning, _x2520-1328 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-05318-5 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
912 | _aZDB-2-SOB | ||
942 | _cSPRINGER | ||
999 |
_c172702 _d172702 |