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020 _a9783030670245
_9978-3-030-67024-5
024 7 _a10.1007/978-3-030-67024-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aBrazdil, Pavel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMetalearning
_h[electronic resource] :
_bApplications to Automated Machine Learning and Data Mining /
_cby Pavel Brazdil, Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren.
250 _a2nd ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXII, 346 p. 90 illus., 45 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 offers a comprehensive and thorough introduction to almost all aspects of metalearning and automated machine learning (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. 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, 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 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.
650 0 _aArtificial intelligence.
650 0 _aData mining.
650 0 _aMachine learning.
650 1 4 _aArtificial Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aMachine Learning.
700 1 _avan Rijn, Jan N.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aSoares, Carlos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aVanschoren, Joaquin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030670238
776 0 8 _iPrinted edition:
_z9783030670252
776 0 8 _iPrinted edition:
_z9783030670269
830 0 _aCognitive Technologies,
_x2197-6635
856 4 0 _uhttps://doi.org/10.1007/978-3-030-67024-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
942 _cSPRINGER
999 _c178975
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