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Learning Markov Logic Network Struct...

ProQuest Information and Learning Co.

Learning Markov Logic Network Structure by Template Constructing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Learning Markov Logic Network Structure by Template Constructing./
Author:	Tong, Yingbei.
Description:	1 online resource (30 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-05.
Contained By:	Masters Abstracts International56-05(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369884326

Learning Markov Logic Network Structure by Template Constructing.
Tong, Yingbei.

Learning Markov Logic Network Structure by Template Constructing. - 1 online resource (30 pages)

Source: Masters Abstracts International, Volume: 56-05.

Thesis (M.S.)--Iowa State University, 2017.

Includes bibliographical references

Markov logic networks (MLNs) are a statistical relational model that incorporates first- order logic and probability by attaching weights to first-order clauses. However, due to the large search space, the structure learning of MLNs is a computationally expensive problem. In this paper, we present a new algorithm for learning the structure of Markov Logic Network by directly utilizing the data to construct the candidate clauses. Our approach makes use of a Markov Network learning algorithm to construct a template network. We then apply the template to guide the candidate clauses construction process. The experimental results demonstrate that our algorithm is promising.

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

Mode of access: World Wide Web

ISBN: 9781369884326Subjects--Topical Terms:

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

554714
Electronic books.

Learning Markov Logic Network Structure by Template Constructing.
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100 1 $a Tong, Yingbei. $3 1190931
245 1 0 $a Learning Markov Logic Network Structure by Template Constructing.
264 0 $c 2017
300 $a 1 online resource (30 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: Masters Abstracts International, Volume: 56-05.
500 $a Adviser: Jin Tian.
502 $a Thesis (M.S.)--Iowa State University, 2017.
504 $a Includes bibliographical references
520 $a Markov logic networks (MLNs) are a statistical relational model that incorporates first- order logic and probability by attaching weights to first-order clauses. However, due to the large search space, the structure learning of MLNs is a computationally expensive problem. In this paper, we present a new algorithm for learning the structure of Markov Logic Network by directly utilizing the data to construct the candidate clauses. Our approach makes use of a Markov Network learning algorithm to construct a template network. We then apply the template to guide the candidate clauses construction process. The experimental results demonstrate that our algorithm is promising.
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 Iowa State University. $b Computer Science. $3 1190932
773 0 $t Masters Abstracts International $g 56-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10271469 $z click for full text (PQDT)

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