DocumentCode
2386408
Title
Research on Statistical Relational Learning and Rough Set in SRL
Author
Chen, Fei
Author_Institution
Univ. of Maryland Baltimore County, Baltimore
fYear
2007
fDate
2-4 Nov. 2007
Firstpage
227
Lastpage
227
Abstract
Statistical relational learning constructs statistical models from relational databases, combining the powers of relational learning and statistical learning. Its strong ability and special property make statistical relational learning become one of the important areas in machine learning. In this paper, the general concepts and characteristics of statistical relational learning are presented firstly. Then some major branches of this newly emerging field are discussed, including logic and rule-based approaches, frame and object-oriented approaches, and several other important approaches. After that some methods of applying rough set in statistical relational learning are described, such as gRS-ILP and VPRSILP. Finally applications of statistical relational learning are briefly introduced and some future directions of statistical relational learning and the prospects of rough set in this area are pointed out.
Keywords
formal logic; learning (artificial intelligence); relational databases; rough set theory; VPRSILP; gRS-ILP; logic approach; machine learning; object-oriented approach; relational databases; rough set; rule-based approach; statistical learning; statistical models; statistical relational learning; Citation analysis; Computer science; Information analysis; Machine learning; Object oriented modeling; Power engineering computing; Probabilistic logic; Relational databases; Robustness; USA Councils;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location
Fremont, CA
Print_ISBN
978-0-7695-3032-1
Type
conf
DOI
10.1109/GrC.2007.137
Filename
4403099
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