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» Learning Bayesian Networks from Incomplete Databases
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COLING
2000
14 years 11 months ago
Word Sense Disambiguation of Adjectives Using Probabilistic Networks
In this paper, word sense dismnbiguation (WSD) accuracy achievable by a probabilistic classifier, using very milfimal training sets, is investigated. \Ve made the assuml)tiou that...
Gerald Chao, Michael G. Dyer
CIKM
2010
Springer
14 years 8 months ago
Discriminative factored prior models for personalized content-based recommendation
Most existing content-based filtering approaches including Rocchio, Language Models, SVM, Logistic Regression, Neural Networks, etc. learn user profiles independently without ca...
Lanbo Zhang, Yi Zhang 0001
VLDB
1998
ACM
147views Database» more  VLDB 1998»
15 years 2 months ago
Scalable Techniques for Mining Causal Structures
Mining for association rules in market basket data has proved a fruitful areaof research. Measures such as conditional probability (confidence) and correlation have been used to i...
Craig Silverstein, Sergey Brin, Rajeev Motwani, Je...
AAAI
2008
15 years 7 days ago
Markov Blanket Feature Selection for Support Vector Machines
Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery ...
Jianqiang Shen, Lida Li, Weng-Keen Wong
KDD
2003
ACM
175views Data Mining» more  KDD 2003»
15 years 10 months ago
Time and sample efficient discovery of Markov blankets and direct causal relations
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...