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Hadoop Based Algorithm for Computing...

ProQuest Information and Learning Co.

Hadoop Based Algorithm for Computing Linear Regression.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Hadoop Based Algorithm for Computing Linear Regression./
作者:	Wang, Tian.
面頁冊數:	1 online resource (57 pages)
附註:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355157314

Hadoop Based Algorithm for Computing Linear Regression.
Wang, Tian.

Hadoop Based Algorithm for Computing Linear Regression. - 1 online resource (57 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)--Purdue University, 2017.

Includes bibliographical references

As machine learning and big data analysis play a more and more important role in both industry and academia, researchers correspondingly spend a large amount of time trying to find those accurate models that could help researchers predict the trend of a certain phenomenon. Current packages and functions in R, Hadoop and RHadoop require accessing the entire data set each time when a new set of parameters need to be evaluated. This is extremely time-consuming when data is big and disk I/O is slow. This study implemented an one-read-multiple-evaluation technique that can greatly reduce time needed to find the best model from multiple sets of parameters. In the testing RHadoop environment, the proposed approach showed that finding the best Box-Cox transformed linear model from 41 potential parameters was about 25 times faster than the linear models on RHadoop when the training datasets is about 12.4 GB. Results also showed the scheme is scalable when the size of data is bigger and more sets of parameters need to be compared.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355157314Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Hadoop Based Algorithm for Computing Linear Regression.
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500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Advisers: Baijian Yang; TongLin Zhang.
502 $a Thesis (M.S.)--Purdue University, 2017.
504 $a Includes bibliographical references
520 $a As machine learning and big data analysis play a more and more important role in both industry and academia, researchers correspondingly spend a large amount of time trying to find those accurate models that could help researchers predict the trend of a certain phenomenon. Current packages and functions in R, Hadoop and RHadoop require accessing the entire data set each time when a new set of parameters need to be evaluated. This is extremely time-consuming when data is big and disk I/O is slow. This study implemented an one-read-multiple-evaluation technique that can greatly reduce time needed to find the best model from multiple sets of parameters. In the testing RHadoop environment, the proposed approach showed that finding the best Box-Cox transformed linear model from 41 potential parameters was about 25 times faster than the linear models on RHadoop when the training datasets is about 12.4 GB. Results also showed the scheme is scalable when the size of data is bigger and more sets of parameters need to be compared.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
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710 2 $a Purdue University. $b Computer and Information Technology. $3 1180535
773 0 $t Masters Abstracts International $g 56-06(E).
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