東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Supervised machine learning = optimi...

Kolosova, Tanya.

Linked to FindBook

Google Book

Amazon

博客來

Supervised machine learning = optimization framework and applications with SAS and R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Supervised machine learning/ Tanya Kolosova, Samuel Berestizhevsky.
Reminder of title:	optimization framework and applications with SAS and R /
Author:	Kolosova, Tanya.
other author:	Berestizhevsky, Samuel.
Published:	Boca Raton, FL :CRC Press, : 2021.,
Description:	1 online resource (xxiv, 160 p.)
Notes:	"A Chapman & Hall book."
Subject:	Supervised learning (Machine learning) -
Online resource:	https://www.taylorfrancis.com/books/9780429297595
ISBN:	9780429297595

Supervised machine learning = 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:

731226
Supervised learning (Machine learning)

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

Dewey Class. No.: 006.3/1

Supervised machine learning = optimization framework and applications with SAS and R /
LDR:02467cmm a2200373 a 4500 001 2311265
003 FlBoTFG
005 20201119011332.0
006 m o d
007 cr cnu---unuuu
008 230722s2021 flu ob 001 0 eng d
020 $a 9780429297595 $q (electronic book)
020 $a 0429297599 $q (electronic book)
020 $a 9781000176810 $q (electronic book)
020 $a 1000176819 $q (electronic book)
020 $a 9781000176827 $q (Mobipocket)
020 $a 1000176827 $q (Mobipocket)
020 $a 9781000176834 $q (EPUB)
020 $a 1000176835 $q (EPUB)
020 $z 9780367538828 $q (pbk.)
020 $z 9780367277321 $q (hbk.)
035 $a 9780429297595
040 $a OCoLC-P $b eng $c OCoLC-P
041 0 $a eng
050 4 $a Q325.75 $b .K65 2021eb
082 0 4 $a 006.3/1 $2 23
100 1 $a Kolosova, Tanya. $3 3620691
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 731226
650 0 $a Program transformation (Computer programming) $3 907578
650 0 $a SAS (Computer program language) $3 1234898
650 0 $a R (Computer program language) $3 784593
700 1 $a Berestizhevsky, Samuel. $3 3620692
856 4 0 $u https://www.taylorfrancis.com/books/9780429297595