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Metalearningapplications to automated machine learning and data mining /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Metalearningby Pavel Brazdil ... [et al.].
其他題名:	applications to automated machine learning and data mining /
其他作者:	Brazdil, Pavel.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xii, 346 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-67024-5
ISBN:	9783030670245$q(electronic bk.)

Metalearningapplications to automated machine learning and data mining /
Metalearningapplications to automated machine learning and data mining /[electronic resource] :by Pavel Brazdil ... [et al.]. - Second edition. - Cham :Springer International Publishing :2022. - xii, 346 p. :ill. (some col.), digital ;24 cm. - Cognitive technologies,2197-6635. - Cognitive technologies..

Introduction -- 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.

Open access.

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.

ISBN: 9783030670245$q(electronic bk.)

Standard No.: 10.1007/978-3-030-67024-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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505 0 $a Introduction -- 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 $a Open access.
520 $a 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.
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