Automated Machine Learning

Methods, Systems, Challenges

Frank Hutter, Lars Kotthoff, Joaquin Vanschoren (Herausgeber)

Buch | Hardcover

XIV, 219 Seiten

2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-030-05317-8 (ISBN)

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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.

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.

"This interesting collection should be useful for AutoML researchers seeking an overview and comprehensive bibliography." (Anoop Malaviya, Computing Reviews, June 14, 2021)

Erscheinungsdatum	30.05.2019
Reihe/Serie	The Springer Series on Challenges in Machine Learning
Zusatzinfo	XIV, 219 p. 54 illus., 45 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	609 g
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Architecture search • Automated data science • Automated machine learning • Deep learning • Feature Selection • machine learning • Machine learning pipeline optimization • Machine learning software • Off-the-shelf machine learning • open access • Preprocessing • Selecting a machine learning algorithm • Tuning Hyperparameters
ISBN-10	3-030-05317-2 / 3030053172
ISBN-13	978-3-030-05317-8 / 9783030053178
Zustand	Neuware