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Automated Machine Learning
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
other
Author(s):
Lars Kotthoff
,
Chris Thornton
,
Holger H. Hoos
,
Frank Hutter
,
Kevin Leyton-Brown
Publication date
(Online):
May 18 2019
Publisher:
Springer International Publishing
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There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
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Business Model Innovation in engineering
Author and book information
Book Chapter
Publication date (Print):
2019
Publication date (Online):
May 18 2019
Pages
: 81-95
DOI:
10.1007/978-3-030-05318-5_4
SO-VID:
4a4ec654-22ed-4e57-b738-e6b8cfce86ed
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Book chapters
pp. C1
Correction to: Neural Architecture Search
pp. 3
Hyperparameter Optimization
pp. 35
Meta-Learning
pp. 63
Neural Architecture Search
pp. 81
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
pp. 97
Hyperopt-Sklearn
pp. 113
Auto-sklearn: Efficient and Robust Automated Machine Learning
pp. 135
Towards Automatically-Tuned Deep Neural Networks
pp. 151
TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning
pp. 161
The Automatic Statistician
pp. 177
Analysis of the AutoML Challenge Series 2015–2018
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