Metalearning
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 oth...
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| Médium: | Online |
| Jazyk: | angličtina |
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Springer Nature
2022
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| On-line přístup: | ONIX_20220314_9783030670245_34 |
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| _version_ | 1869523376217784320 |
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| author | Brazdil, Pavel van Rijn, Jan N. Soares, Carlos Vanschoren, Joaquin |
| author_browse | Brazdil, Pavel Soares, Carlos Vanschoren, Joaquin van Rijn, Jan N. |
| author_facet | Brazdil, Pavel van Rijn, Jan N. Soares, Carlos Vanschoren, Joaquin |
| author_sort | Brazdil, Pavel |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-79344 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-793442025-07-30T11:55:05Z Metalearning Brazdil, Pavel van Rijn, Jan N. Soares, Carlos Vanschoren, Joaquin Metalearning Automating Machine Learning (AutoML) Machine Learning Artificial Intelligence algorithm selection algorithm recommendation algorithm configuration hyperparameter optimization automating the workflow/pipeline design metalearning in ensemble construction metalearning in deep neural networks transfer learning algorithm recommendation for data streams automating data science Open Access thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning 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. 2022-03-16T04:01:40Z 2022-03-16T04:01:40Z 2022-03-15T07:52:52Z 2022 book ONIX_20220314_9783030670245_34 ONIX_20220314_9783030670245_34 OCN: 1313550840 https://library.oapen.org/handle/20.500.12657/53319 9783030670245 https://directory.doabooks.org/handle/20.500.12854/79344 eng Cognitive Technologies open access image/jpeg image/jpeg image/jpeg image/jpeg n/a n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/53319/1/978-3-030-67024-5.pdf https://library.oapen.org/bitstream/20.500.12657/53319/1/978-3-030-67024-5.pdf https://library.oapen.org/bitstream/20.500.12657/53319/1/978-3-030-67024-5.pdf https://library.oapen.org/bitstream/20.500.12657/53319/1/978-3-030-67024-5.pdf Springer Nature Springer 10.1007/978-3-030-67024-5 10.1007/978-3-030-67024-5 9fa3421d-f917-4153-b9ab-fc337c396b5a Nederlandse Organisatie voor Wetenschappelijk Onderzoek 9783030670245 Dutch Research Council (NWO) Springer 346 Cham 612.001.206 open access |
| spellingShingle | Metalearning Automating Machine Learning (AutoML) Machine Learning Artificial Intelligence algorithm selection algorithm recommendation algorithm configuration hyperparameter optimization automating the workflow/pipeline design metalearning in ensemble construction metalearning in deep neural networks transfer learning algorithm recommendation for data streams automating data science Open Access thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Brazdil, Pavel van Rijn, Jan N. Soares, Carlos Vanschoren, Joaquin Metalearning |
| title | Metalearning |
| title_full | Metalearning |
| title_fullStr | Metalearning |
| title_full_unstemmed | Metalearning |
| title_short | Metalearning |
| title_sort | metalearning |
| topic | Metalearning Automating Machine Learning (AutoML) Machine Learning Artificial Intelligence algorithm selection algorithm recommendation algorithm configuration hyperparameter optimization automating the workflow/pipeline design metalearning in ensemble construction metalearning in deep neural networks transfer learning algorithm recommendation for data streams automating data science Open Access thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Metalearning Automating Machine Learning (AutoML) Machine Learning Artificial Intelligence algorithm selection algorithm recommendation algorithm configuration hyperparameter optimization automating the workflow/pipeline design metalearning in ensemble construction metalearning in deep neural networks transfer learning algorithm recommendation for data streams automating data science Open Access thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20220314_9783030670245_34 |
| work_keys_str_mv | AT brazdilpavel metalearning AT vanrijnjann metalearning AT soarescarlos metalearning AT vanschorenjoaquin metalearning |