Automated Machine Learning

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Title

Automated Machine Learning

Subject

Computer science; Artificial intelligence; Optical data processing; Pattern recogn

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.

Creator

Hutter, Frank (editor) Kotthoff, Lars (editor) Vanschoren, Joaquin (editor)

Source

http://library.oapen.org/handle/20.500.12657/23012

Publisher

Springer Nature

Date

2019

Contributor

upload by novit

Rights

http://creativecommons.org/licenses/by/4.0/

Format

Pdf

Language

English

Type

Textbooks

Identifier

10.1007/978-3-030-05318-5

Coverage

Artificial intelligence Pattern recognition Image processing

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