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14300372
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20200730060539.0
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190517s2019 gw | o |||| 0|eng d
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9783030053185
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7
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10.1007/978-3-030-05318-5
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doi
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(DE-He213)978-3-030-05318-5
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14300372
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Q334-342
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4
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006.3
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23
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0
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Automated Machine Learning
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[electronic resource] :
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Methods, Systems, Challenges /
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edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
264
1
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Cham :
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Springer International Publishing :
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Imprint: Springer,
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2019.
300
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XIV, 219 p. 54 illus., 45 illus. in color.
336
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text
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txt
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rdacontent
337
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computer
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c
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rdamedia
338
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online resource
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cr
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rdacarrier
347
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text file
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PDF
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rda
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1
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The Springer Series on Challenges in Machine Learning,
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2520-131X
505
0
|a
1 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
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Access restricted by licensing agreement.
520
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This 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.
590
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Access is available to the Yale community.
650
0
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Artificial intelligence.
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http://id.loc.gov/authorities/subjects/sh85008180
650
0
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Computer vision.
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http://id.loc.gov/authorities/subjects/sh85029549
650
0
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Optical pattern recognition.
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http://id.loc.gov/authorities/subjects/sh85095159
700
1
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Hutter, Frank.
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edt
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http://id.loc.gov/vocabulary/relators/edt
700
1
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Kotthoff, Lars.
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edt
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http://id.loc.gov/vocabulary/relators/edt
700
1
|a
Vanschoren, Joaquin.
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(orcid)0000-0001-7044-9805
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https://orcid.org/0000-0001-7044-9805
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edt
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http://id.loc.gov/vocabulary/relators/edt
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http://id.loc.gov/authorities/names/nb2017001619
710
2
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SpringerLink (Online service)
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http://id.loc.gov/authorities/names/no2005046756
730
0
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Springer ENIN.
773
0
|t
Springer eBooks
776
0
8
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Printed edition:
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9783030053178
776
0
8
|i
Printed edition:
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9783030053192
830
0
|a
Springer series on challenges in machine learning.
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http://id.loc.gov/authorities/names/no2018053726
852
8
0
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Online resource
856
4
0
|y
Online book
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https://yale.idm.oclc.org/login?URL=https://doi.org/10.1007/978-3-030-05318-5
901
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Q334-342
902
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Yale Internet Resource
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Yale Internet Resource >> None|DELIM|14304866
905
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online resource
907
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2019-06-04T12:46:17.000Z
946
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DO NOT EDIT. DO NOT EXPORT.
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4
0
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https://doi.org/10.1007/978-3-030-05318-5