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03726nam a22005535i 4500 |
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978-3-030-05318-5 |
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DE-He213 |
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20210619014437.0 |
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cr nn 008mamaa |
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190517s2019 gw | s |||| 0|eng d |
| 020 |
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|a 9783030053185
|9 978-3-030-05318-5
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|a 10.1007/978-3-030-05318-5
|2 doi
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| 245 |
1 |
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|a Automated Machine Learning
|h [electronic resource] :
|b Methods, Systems, Challenges /
|c edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
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| 250 |
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|a 1st ed. 2019.
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| 264 |
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1 |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2019.
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| 300 |
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|a XIV, 219 p. 54 illus., 45 illus. in color.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a The Springer Series on Challenges in Machine Learning,
|x 2520-131X
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| 505 |
0 |
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|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.
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| 506 |
0 |
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|a Open Access
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| 520 |
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|a 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.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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0 |
|a Optical data processing.
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| 650 |
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0 |
|a Pattern recognition.
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| 650 |
1 |
4 |
|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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| 650 |
2 |
4 |
|a Image Processing and Computer Vision.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I22021
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| 650 |
2 |
4 |
|a Pattern Recognition.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
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| 700 |
1 |
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|a Hutter, Frank.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Kotthoff, Lars.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Vanschoren, Joaquin.
|e editor.
|0 (orcid)0000-0001-7044-9805
|1 https://orcid.org/0000-0001-7044-9805
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783030053178
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783030053192
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| 830 |
|
0 |
|a The Springer Series on Challenges in Machine Learning,
|x 2520-131X
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-05318-5
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| 912 |
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| 912 |
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|a ZDB-2-SXCS
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| 912 |
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|a ZDB-2-SOB
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
|
