Editors:
- Presents a tutorial-level overview of the methods underlying automatic machine learning, enabling readers to easily understand the key concepts behind AutoML
- Offers a comprehensive collection of in-depth descriptions of AutoML systems, allowing readers to see how the key concepts have been implemented in the context of actual systems
- Discusses an independent international competition of many different systems, providing an independent evaluation of pros and cons of different AutoML approaches
Part of the book series: The Springer Series on Challenges in Machine Learning (SSCML)
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Table of contents (11 chapters)
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Front Matter
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AutoML Methods
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Front Matter
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AutoML Systems
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Front Matter
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AutoML Challenges
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Front Matter
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About this book
Keywords
- Machine learning
- Automated machine learning
- Automated data science
- Off-the-shelf machine learning
- Machine learning software
- Selecting a machine learning algorithm
- Tuning Hyperparameters
- Feature selection
- Preprocessing
- Deep learning
- Architecture search
- Machine learning pipeline optimization
- Open Access
Reviews
“This interesting collection should be useful for AutoML researchers seeking an overview and comprehensive bibliography.” (Anoop Malaviya, Computing Reviews, June 14, 2021)
Editors and Affiliations
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Department of Computer Science, University of Freiburg, Freiburg, Germany
Frank Hutter
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University of Wyoming, Laramie, USA
Lars Kotthoff
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Eindhoven University of Technology, Eindhoven, The Netherlands
Joaquin Vanschoren
Bibliographic Information
Book Title: Automated Machine Learning
Book Subtitle: Methods, Systems, Challenges
Editors: Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
Series Title: The Springer Series on Challenges in Machine Learning
DOI: https://doi.org/10.1007/978-3-030-05318-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2019
Hardcover ISBN: 978-3-030-05317-8Published: 28 May 2019
eBook ISBN: 978-3-030-05318-5Published: 17 May 2019
Series ISSN: 2520-131X
Series E-ISSN: 2520-1328
Edition Number: 1
Number of Pages: XIV, 219
Number of Illustrations: 9 b/w illustrations, 45 illustrations in colour
Topics: Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition