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Collaborative ranking and collaborat...

Chen, Zheng.

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Collaborative ranking and collaborative clustering.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Collaborative ranking and collaborative clustering./
作者:	Chen, Zheng.
面頁冊數:	177 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
Contained By:	Dissertation Abstracts International74-05B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3549042
ISBN:	9781267851499

Collaborative ranking and collaborative clustering.
Chen, Zheng.

Collaborative ranking and collaborative clustering. - 177 p.

Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.

Thesis (Ph.D.)--City University of New York, 2013.

Ranking and clustering are two important problems in machine learning and have wide applications in Natural Language Processing (NLP). A ranking problem is typically formulated as ranking a collection of candidate "objects" with respect to a "query" while a clustering problem is formulated as organizing a set of instances into groups such that members in each group share some similarity while members across groups are dissimilar. In this thesis, we introduce collaborative schemes into ranking and clustering problems, and name them as "collaborative ranking" and "collaborative clustering" respectively. Contrast to the tradition non-collaborative schemes, collaborative ranking leverages strengths from multiple query collaborators and ranker collaborators while collaborative clustering leverages strengths from multiple instance collaborators and clusterer collaborators. We select several typical NLP problems as our case studies including entity linking, document clustering and name entity clustering.

ISBN: 9781267851499Subjects--Topical Terms:

626642
Computer Science.

Collaborative ranking and collaborative clustering.
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520 $a Ranking and clustering are two important problems in machine learning and have wide applications in Natural Language Processing (NLP). A ranking problem is typically formulated as ranking a collection of candidate "objects" with respect to a "query" while a clustering problem is formulated as organizing a set of instances into groups such that members in each group share some similarity while members across groups are dissimilar. In this thesis, we introduce collaborative schemes into ranking and clustering problems, and name them as "collaborative ranking" and "collaborative clustering" respectively. Contrast to the tradition non-collaborative schemes, collaborative ranking leverages strengths from multiple query collaborators and ranker collaborators while collaborative clustering leverages strengths from multiple instance collaborators and clusterer collaborators. We select several typical NLP problems as our case studies including entity linking, document clustering and name entity clustering.
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790 1 0 $a Wang, Wen $e committee member
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