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020			_a9783030670245
024	7		_a10.1007/978-3-030-67024-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aBrazdil, Pavel B., _4http://id.loc.gov/vocabulary/relators/aut _9683423 _d1945-
245	1	0	_aMetalearning : _bApplications to Automated Machine Learning and Data Mining _cby Pavel Brazdil, Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren
250			_aSecond edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XII, 346 páginas) _b90 ilustraciones, 45 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aCognitive Technologies _x2197-6635
505	0		_aIntroduction -- Part I, Basic Architecture of Metalearning and AutoML Systems -- Metalearning Approaches for Algorithm Selection I -- Evaluating Recommendations of Metalearning / AutoML Systems -- Metalearning Approaches for Algorithm Selection II -- Automating Machine Learning (AutoML) and Algorithm Configuration -- Dataset Characteristics (Metafeatures) -- Automating the Workflow / Pipeline Design -- Part II, Extending the Architecture of Metalearning and AutoML Systems -- Setting Up Configuration Spaces and Experiments -- Using Metalearning in the Construction of Ensembles -- Algorithm Recommendation for Data Streams -- Transfer of Metamodels Across Tasks -- Automating Data Science -- Automating the Design of Complex Systems -- Repositories of Experimental Results (OpenML) -- Learning from Metadata in Repositories.
506	0		_aOpen Access
520			_aThis 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.
988			_aSpringer_Computer_2022
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_avan Rijn, Jan N _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSoares, Carlos _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aVanschoren, Joaquin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030670238
776	0	8	_iPrinted edition: _z9783030670252
776	0	8	_iPrinted edition: _z9783030670269
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-67024-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2022 _dz _eh _zSI