Overview
- 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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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
About this book
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
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