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Scaling activity discovery and recog...

ProQuest Information and Learning Co.

Scaling activity discovery and recognition to large, complex datasets.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Scaling activity discovery and recognition to large, complex datasets./
作者:	Rashidi, Parisa.
面頁冊數:	1 online resource (275 pages)
附註:	Source: Dissertation Abstracts International, Volume: 72-09, Section: B, page: 5406.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781124722054

Scaling activity discovery and recognition to large, complex datasets.
Rashidi, Parisa.

Scaling activity discovery and recognition to large, complex datasets. - 1 online resource (275 pages)

Source: Dissertation Abstracts International, Volume: 72-09, Section: B, page: 5406.

Thesis (Ph.D.)--Washington State University, 2011.

Includes bibliographical references

In the past decade, activity discovery and recognition has been studied by many researchers. However there are still many challenges to be addressed before deploying such technologies in the real world. We try to address some of those challenges in order to achieve a more scalable solution that can be used in the real world.

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

Mode of access: World Wide Web

ISBN: 9781124722054Subjects--Topical Terms:

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

554714
Electronic books.

Scaling activity discovery and recognition to large, complex datasets.
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040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Rashidi, Parisa. $3 1188717
245 1 0 $a Scaling activity discovery and recognition to large, complex datasets.
264 0 $c 2011
300 $a 1 online resource (275 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 72-09, Section: B, page: 5406.
500 $a Adviser: Diane J. Cook.
502 $a Thesis (Ph.D.)--Washington State University, 2011.
504 $a Includes bibliographical references
520 $a In the past decade, activity discovery and recognition has been studied by many researchers. However there are still many challenges to be addressed before deploying such technologies in the real world. We try to address some of those challenges in order to achieve a more scalable solution that can be used in the real world.
520 $a First, we introduce a novel data mining method called the continuous Varied order Sequence Mining method (DVSM). It is able to discover activity pattern sequences, even if those patterns are disrupted or have varied step orders. We further extend DVSM into another data mining method called the Continuous varied Order Multi threshold activity discovery method (COM). COM is able to handle issues such as rare events across time and space. Furthermore, for discovering patterns in a real time manner, we extend COM as a stream mining method called StreamCOM.
520 $a In addition to discovering activity patterns, we propose several methods for transferring discovered patterns from one setting to another. We propose methods for transferring activity models of one resident to another, activity models of a physical space to another, and activity models of multiple spaces to another. We also show a method for selecting the most promising sources when multiple sources are available.
520 $a In order to further expedite the learning process, we also propose two novel active learning methods to construct generic active learning queries. Our generic queries are shorter and more intuitive and encompass many similar cases. We show how we can achieve a higher accuracy rate with fewer queries compared to traditional active learning methods.
520 $a All of our methods have been tested on real data collected from CASAS smart apartments. In several cases, we also tested our algorithms on various other datasets.
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
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Washington State University. $b Computer Science. $3 1186533
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3460419 $z click for full text (PQDT)

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