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New Models and Algorithms for Data A...

ProQuest Information and Learning Co.

New Models and Algorithms for Data Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	New Models and Algorithms for Data Analysis./
作者:	Fish, Benjamin.
面頁冊數:	1 online resource (130 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
標題:	Mathematics. -
電子資源:	click for full text (PQDT)
ISBN:	9780438265370

New Models and Algorithms for Data Analysis.
Fish, Benjamin.

New Models and Algorithms for Data Analysis. - 1 online resource (130 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--University of Illinois at Chicago, 2018.

Includes bibliographical references

In this thesis, we introduce and analyze new models and new algorithms for problems in data analysis. Many new challenges and constraints for data analysis have arisen as data analysis has become increasingly important. We tackle a few of these challenges here.

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

Mode of access: World Wide Web

ISBN: 9780438265370Subjects--Topical Terms:

527692
Mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

New Models and Algorithms for Data Analysis.
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100 1 $a Fish, Benjamin. $3 1193747
245 1 0 $a New Models and Algorithms for Data Analysis.
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300 $a 1 online resource (130 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Adviser: Lev Reyzin.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2018.
504 $a Includes bibliographical references
520 $a In this thesis, we introduce and analyze new models and new algorithms for problems in data analysis. Many new challenges and constraints for data analysis have arisen as data analysis has become increasingly important. We tackle a few of these challenges here.
520 $a First, we explore the challenges that arise when data analysis done adaptively, so that previous inferences guide future analyst's queries. We give faster algorithms for providing accurate responses to data analyst's adaptive queries. Secondly, we initiate the study of the theory for learning from label proportions, which captures voting and similar non-standard settings. We compare this type of learning to classical supervised machine learning, and show examples for which learning from label proportions is possible. Finally, we tackle learning social networks from voting data, where individuals communicate in order to decide their votes. We show that modeling assumptions on how votes are influenced by the network can be brittle: a small change to the model may drastically change hardness of learning the network, as well as change the resulting learned networks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mathematics. $3 527692
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0405
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Illinois at Chicago. $b Mathematics, Statistics, and Computer Science. $3 1179580
773 0 $t Dissertation Abstracts International $g 79-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10956809 $z click for full text (PQDT)

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