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Record Nr. |
UNINA9910337835803321 |
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Autore |
Hutter Frank |
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Titolo |
Automated Machine Learning [[electronic resource] ] : Methods, Systems, Challenges / / edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren |
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Pubbl/distr/stampa |
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Cham, : Springer Nature, 2019 |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XIV, 219 p. 54 illus., 45 illus. in color.) |
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Collana |
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The Springer Series on Challenges in Machine Learning, , 2520-131X |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Optical data processing |
Pattern recognition |
Artificial Intelligence |
Image Processing and Computer Vision |
Pattern Recognition |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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