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Generative Modeling and Unsupervised...

University of California, Los Angeles.

Generative Modeling and Unsupervised Learning in Computer Vision.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Generative Modeling and Unsupervised Learning in Computer Vision./
作者:	Xie, Jianwen.
面頁冊數:	1 online resource (222 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9781339808291

Generative Modeling and Unsupervised Learning in Computer Vision.
Xie, Jianwen.

Generative Modeling and Unsupervised Learning in Computer Vision. - 1 online resource (222 pages)

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2016.

Includes bibliographical references

Developing statistical models and associated learning algorithms for the rich visual patterns in natural images is of fundamental importance for computer vision. More importantly, the endeavor has the potential to enrich our treasured collections of statistical models and expand the already vast reach of machine learning methodologies. Generative models enable us to learn useful features and representations from the natural images in an unsupervised manner. The learned features and representations can be more interpretable and explicit than those learned by the discriminative models, especially if the learned models are sparse. The objective of this dissertation is to learn probabilistic generative models for representing visual patterns in natural images. (Abstract shortened by ProQuest.).

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

Mode of access: World Wide Web

ISBN: 9781339808291Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Generative Modeling and Unsupervised Learning in Computer Vision.
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100 1 $a Xie, Jianwen. $3 1186549
245 1 0 $a Generative Modeling and Unsupervised Learning in Computer Vision.
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300 $a 1 online resource (222 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Adviser: Ying Nian Wu.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2016.
504 $a Includes bibliographical references
520 $a Developing statistical models and associated learning algorithms for the rich visual patterns in natural images is of fundamental importance for computer vision. More importantly, the endeavor has the potential to enrich our treasured collections of statistical models and expand the already vast reach of machine learning methodologies. Generative models enable us to learn useful features and representations from the natural images in an unsupervised manner. The learned features and representations can be more interpretable and explicit than those learned by the discriminative models, especially if the learned models are sparse. The objective of this dissertation is to learn probabilistic generative models for representing visual patterns in natural images. (Abstract shortened by ProQuest.).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Los Angeles. $b Statistics. $3 1186550
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10119595 $z click for full text (PQDT)

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