DocumentCode :
2767955
Title :
A New Topic Filter Based on Maximum Entropy Model
Author :
Chen, Chen ; Liu, Huilin ; Wang, Guoren ; Yu, Lili
Author_Institution :
Key Lab. of Med. Image Comput., Northeastern Univ., Shenyang, China
Volume :
7
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
495
Lastpage :
499
Abstract :
Because of the large web scale and the information requirement for special field, focuse2825453011d search has attracted more and more people. For the complexity of natural language, there are ambiguous for a word itself, and which will take some trouble for topic filter. For the two main problems, false positive and false negative, this paper proposes two new methods separately. By machine learning, we construct a guide model with the maximum entropy principle, by which we can filter the noise pages out easily and by KNN method, the false negative problem will be solved easily. The experiment shows that our model or method really out performs the base-line method.
Keywords :
information filters; learning (artificial intelligence); maximum entropy methods; natural languages; KNN method; base-line method; focuse2825453011d search; information requirement; machine learning; maximum entropy model; natural language; noise pages; topic filter; web scale; Biomedical imaging; Educational institutions; Entropy; Fuzzy systems; Information filtering; Information filters; Laboratories; Machine learning; Search engines; Systems engineering education;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
Type :
conf
DOI :
10.1109/FSKD.2009.709
Filename :
5360059
Link To Document :
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