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Automated machine learning = methods...

Hutter, Frank.

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Automated machine learning = methods, systems, challenges /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated machine learning/ edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
Reminder of title:	methods, systems, challenges /
other author:	Hutter, Frank.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xiv, 219 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-05318-5
ISBN:	9783030053185

Automated machine learning = methods, systems, challenges /
Automated machine learningmethods, systems, challenges /[electronic resource] :edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren. - Cham :Springer International Publishing :2019. - xiv, 219 p. :ill., digital ;24 cm. - The Springer series on challenges in machine learning,2520-131X. - Springer series on challenges in machine learning..

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.

Open access.

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.

ISBN: 9783030053185

Standard No.: 10.1007/978-3-030-05318-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Automated machine learning = methods, systems, challenges /
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245 0 0 $a Automated machine learning $h [electronic resource] : $b methods, systems, challenges / $c edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiv, 219 p. : $b ill., digital ; $c 24 cm.
490 1 $a The Springer series on challenges in machine learning, $x 2520-131X
505 0 $a 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.
506 $a Open access.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Hutter, Frank. $3 3410298
700 1 $a Kotthoff, Lars. $3 3410299
700 1 $a Vanschoren, Joaquin. $3 3200776
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer series on challenges in machine learning. $3 3242855
856 4 0 $u https://doi.org/10.1007/978-3-030-05318-5
950 $a Computer Science (Springer-11645)