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001 978-3-030-05318-5
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005 20240423124950.0
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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
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Springer Series on Challenges in Machine Learning,
_x2520-1328
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
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