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Supervised machine learning[electronic resource] :optimization framework and applications with SAS and R /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.3/1
書名/作者:	Supervised machine learning : optimization framework and applications with SAS and R // Tanya Kolosova, Samuel Berestizhevsky.
作者:	Kolosova, Tanya.
其他作者:	Berestizhevsky, Samuel.
出版者:	Boca Raton, FL : : CRC Press,, 2021.
面頁冊數:	1 online resource (xxiv, 160 p.)
附註:	"A Chapman & Hall book."
標題:	Supervised learning (Machine learning)
標題:	Program transformation (Computer programming)
標題:	SAS (Computer program language)
標題:	R (Computer program language)
ISBN:	9780429297595
ISBN:	0429297599
ISBN:	9781000176810
ISBN:	1000176819
ISBN:	9781000176827
ISBN:	1000176827
ISBN:	9781000176834
ISBN:	1000176835
書目註:	Includes bibliographical references and index.
摘要、提要註:	AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn't ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub.
電子資源:	https://www.taylorfrancis.com/books/9780429297595

Supervised machine learning[electronic resource] :optimization framework and applications with SAS and R /
Kolosova, Tanya.

Supervised machine learningoptimization framework and applications with SAS and R /[electronic resource] :Tanya Kolosova, Samuel Berestizhevsky. - 1st ed. - Boca Raton, FL :CRC Press,2021. - 1 online resource (xxiv, 160 p.)

"A Chapman & Hall book."

Includes bibliographical references and index.

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn't ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub.

ISBN: 9780429297595Subjects--Topical Terms:

712850
Supervised learning (Machine learning)

LC Class. No.: Q325.75 / .K65 2021eb

Dewey Class. No.: 006.3/1

Supervised machine learning[electronic resource] :optimization framework and applications with SAS and R /
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245 1 0 $a Supervised machine learning $h [electronic resource] : $b optimization framework and applications with SAS and R / $c Tanya Kolosova, Samuel Berestizhevsky.
250 $a 1st ed.
260 $a Boca Raton, FL : $b CRC Press, $c 2021.
300 $a 1 online resource (xxiv, 160 p.)
500 $a "A Chapman & Hall book."
504 $a Includes bibliographical references and index.
520 $a AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn't ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub.
588 $a Description based on print version record.
650 0 $a Supervised learning (Machine learning) $3 712850
650 0 $a Program transformation (Computer programming) $3 510772
650 0 $a SAS (Computer program language) $3 488245
650 0 $a R (Computer program language) $3 465792
700 1 $a Berestizhevsky, Samuel. $3 747534
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