國立虎尾科技大學 |

Automated Machine Learning = Methods, Systems, Challenges /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Automated Machine Learning/ edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
其他題名:	Methods, Systems, Challenges /
其他作者:	Hutter, Frank.
面頁冊數:	XIV, 219 p. 54 illus., 45 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	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. - 1st ed. 2019. - XIV, 219 p. 54 illus., 45 illus. in color.online resource. - The Springer Series on Challenges in Machine Learning,2520-131X. - The 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:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Automated Machine Learning = Methods, Systems, Challenges /
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