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scientifique

Metalearning / Pavel Brazdil (2022)  

Public Titre : Metalearning : Applications to automated machine learning and data mining Type de document : Monographie Auteurs : Pavel Brazdil, Auteur ; Jan N. van Rijn, Auteur ; Carlos Soares, Auteur ; Joaquin Vanschoren, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-67024-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chaîne de traitement [Termes IGN] échantillonnage [Termes IGN] modèle stochastique [Termes IGN] ontologie [Termes IGN] optimisation (mathématiques) [Termes IGN] régression [Termes IGN] science des données [Termes IGN] série temporelle Résumé : (éditeur) This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence. Note de contenu : 1- Basic concepts and architecture 2- Advanced techniques and methods 3- Organizing and Exploiting Metadata Numéro de notice : 28698 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-67024-5 En ligne : https://doi.org/10.1007/978-3-030-67024-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100469

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