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Automated machine learning / Frank Hutter (2019)  

Public Titre : Automated machine learning : methods, systems, challenges Type de document : Monographie Auteurs : Frank Hutter, Éditeur scientifique ; Lars Kotthoff, Éditeur scientifique ; Joaquin Vanschoren, Éditeur scientifique Editeur : Springer Nature Année de publication : 2019 Collection : The Springer Series on Challenges in Machine Learning SSCML, ISSN 2520-1328 Importance : 219 p. ISBN/ISSN/EAN : 978-3-030-05318-5 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme d'apprentissage [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] optimisation (mathématiques) Index. décimale : 26.40 Intelligence artificielle Résumé : (Editeur) 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. Note de contenu : AUTOML METHODS - Hyperparameter Optimization - Meta-Learning - Neural Architecture Search AUTOML SYSTEMS - Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA - Hyperopt-Sklearn - Auto-sklearn: Efficient and Robust Automated Machine Learning - Towards Automatically-Tuned Deep Neural Networks - TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning - The Automatic Statistician AUTOML CHALLENGES - Analysis of the AutoML Challenge Series 2015–2018 - Correction to: Neural Architecture Search Numéro de notice : 26299 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007%2F978-3-030-05318-5 Date de publication en ligne : 04/02/2020 En ligne : https://link.springer.com/book/10.1007%2F978-3-030-05318-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95032