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Content Provider | IEEE Xplore Digital Library |
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Author | Chen Chen Huilin Liu Guoren Wang Lili Yu |
Copyright Year | 2009 |
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. |
Starting Page | 495 |
Ending Page | 499 |
File Size | 454355 |
Page Count | 5 |
File Format | |
ISBN | 9780769537351 |
DOI | 10.1109/FSKD.2009.709 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-08-14 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Laboratories Machine learning Search engines Information filters Educational institutions Entropy Information filtering Systems engineering education Fuzzy systems Biomedical imaging |
Content Type | Text |
Resource Type | Article |
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