Automated Machine Learning - Methods, Systems, Challenges (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	The Springer Series on Challenges in Machine Learning
Release date:	May 2019
First published:	2019
Editors:	Frank Hutter • Lars Kotthoff • Joaquin Vanschoren
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Hardcover
Pages:	219
Edition:	1st ed. 2019
ISBN-13:	978-3-03-005317-8
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	3-03-005317-2
Barcode:	9783030053178

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