Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICMLC.2012.6359008
Title: Probabilistic reasoning on background net: An application to text categorization
Authors: Lo, S.-L.
Ding, L. 
Keywords: Acceptance measure
Background net
Personalized articles selection
Probabilistic reasoning
Text categorization
Issue Date: 2012
Source: Lo, S.-L.,Ding, L. (2012). Probabilistic reasoning on background net: An application to text categorization. Proceedings - International Conference on Machine Learning and Cybernetics 2 : 688-694. ScholarBank@NUS Repository. https://doi.org/10.1109/ICMLC.2012.6359008
Abstract: Background net previously proposed is a novel approach for capturing and representing background information as a knowledge background accumulated through incremental learning on articles. As a continued study on background net, this article proposes a probabilistic reasoning on background nets by defining new acceptance measure based on conditional probabilities. Experiments on text categorization using representative data sets show that our approach, without requiring great effort in preprocessing, achieves competitive performance compared with Naive Bayes, kNN, and SVM methods. © 2012 IEEE.
Source Title: Proceedings - International Conference on Machine Learning and Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/114679
ISBN: 9781467314855
ISSN: 2160133X
DOI: 10.1109/ICMLC.2012.6359008
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