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Collaborative ranking and collaborat...

Chen, Zheng.

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Collaborative ranking and collaborative clustering.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Collaborative ranking and collaborative clustering./
Author:	Chen, Zheng.
Description:	177 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
Contained By:	Dissertation Abstracts International74-05B(E).
Subject:	Computer Science. -
Online resource:	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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008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781267851499
035 $a (UMI)AAI3549042
035 $a AAI3549042
040 $a UMI $c UMI
100 1 $a Chen, Zheng. $3 1043835
245 1 0 $a Collaborative ranking and collaborative clustering.
300 $a 177 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
500 $a Adviser: Heng Ji.
502 $a Thesis (Ph.D.)--City University of New York, 2013.
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.
590 $a School code: 0046.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a City University of New York. $b Computer Science. $3 1029886
773 0 $t Dissertation Abstracts International $g 74-05B(E).
790 1 0 $a Ji, Heng, $e advisor
790 1 0 $a Rosenberg, Andrew $e committee member
790 1 0 $a Haralick, Robert $e committee member
790 1 0 $a Huang, Liang $e committee member
790 1 0 $a Florian, Radu $e committee member
790 1 0 $a Wang, Wen $e committee member
790 $a 0046
791 $a Ph.D.
792 $a 2013
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3549042