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000890962 019__ $$a1105186490
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000890962 0247_ $$a10.1007/978-3-030-05318-5$$2doi
000890962 0247_ $$a10.1007/978-3-030-05
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000890962 24500 $$aAutomated machine learning :$$bmethods, systems, challenges /$$cFrank Hutter, Lars Kotthoff, Joaquin Vanschoren, editors.
000890962 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000890962 300__ $$a1 online resource (xiv, 219 pages) :$$billustrations.
000890962 336__ $$atext$$btxt$$2rdacontent
000890962 337__ $$acomputer$$bc$$2rdamedia
000890962 338__ $$aonline resource$$bcr$$2rdacarrier
000890962 4901_ $$aThe Springer series on challenges in machine learning,$$x2520-131X
000890962 5050_ $$a1 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.
000890962 506__ $$aAccess limited to authorized users.
000890962 520__ $$aThis 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.
000890962 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 19, 2019).
000890962 650_0 $$aMachine learning.
000890962 7001_ $$aHutter, Frank,$$eeditor.
000890962 7001_ $$aKotthoff, Lars,$$eeditor.
000890962 7001_ $$aVanschoren, Joaquin,$$eeditor.
000890962 830_0 $$aSpringer series on challenges in machine learning.
000890962 852__ $$bebk
000890962 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-05318-5$$zOnline Access$$91397441.1
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000890962 980__ $$aEBOOK
000890962 980__ $$aBIB
000890962 982__ $$aEbook
000890962 983__ $$aOnline
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