
Automated Machine Learning
Methods, Systems, Challenges
Springer (Publisher)
Published on 10. July 2019
Book
Hardback
XIV, 219 pages
978-3-030-05317-8 (ISBN)
Description
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.
Reviews / Votes
"This interesting collection should be useful for AutoML researchers seeking an overview and comprehensive bibliography." (Anoop Malaviya, Computing Reviews, June 14, 2021)More details
Product info
Book
Series
Edition
1st ed. 2019
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
9
45 farbige Abbildungen, 32 farbige Tabellen, 9 s/w Abbildungen
32 Tables, color; 45 Illustrations, color; 9 Illustrations, black and white; XIV, 219 p. 54 illus., 45 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 18 mm
Weight
567 gr
ISBN-13
978-3-030-05317-8 (9783030053178)
DOI
10.1007/978-3-030-05318-5
Schweitzer Classification
Content
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.