| 000 | 03899nam a22005775i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-05318-5 | ||
| 003 | DE-He213 | ||
| 005 | 20210511115646.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 190517s2019 gw | 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 | |
| 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 |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aThe Springer Series on Challenges in Machine Learning, _x2520-131X |
|
| 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. _92433 |
|
| 650 | 0 |
_aOptical data processing. _9746 |
|
| 650 | 0 |
_aPattern recognition. _92434 |
|
| 650 | 1 | 4 |
_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000 _92435 |
| 650 | 2 | 4 |
_aImage Processing and Computer Vision. _0https://scigraph.springernature.com/ontologies/product-market-codes/I22021 _9748 |
| 650 | 2 | 4 |
_aPattern Recognition. _0https://scigraph.springernature.com/ontologies/product-market-codes/I2203X _92436 |
| 700 | 1 |
_aHutter, Frank. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _92437 |
|
| 700 | 1 |
_aKotthoff, Lars. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _92438 |
|
| 700 | 1 |
_aVanschoren, Joaquin. _eeditor. _0(orcid)0000-0001-7044-9805 _1https://orcid.org/0000-0001-7044-9805 _4edt _4http://id.loc.gov/vocabulary/relators/edt _92439 |
|
| 710 | 2 |
_aSpringerLink (Online service) _9141 |
|
| 776 | 0 | 8 |
_iPrinted edition: _z9783030053178 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030053192 |
| 830 | 0 |
_aThe Springer Series on Challenges in Machine Learning, _x2520-131X _92440 |
|
| 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 |
_cEBK _w1 _xAdministrator Library _y1 _z Administrator Library |
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| 999 |
_c581 _d581 |
||
| 773 | _tSpringer Nature Open Access eBook | ||