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» Hybrid Learning Scheme for Data Mining Applications
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SDM
2008
SIAM
130views Data Mining» more  SDM 2008»
13 years 9 months ago
Mining Sequence Classifiers for Early Prediction
Supervised learning on sequence data, also known as sequence classification, has been well recognized as an important data mining task with many significant applications. Since te...
Zhengzheng Xing, Jian Pei, Guozhu Dong, Philip S. ...
MSR
2006
ACM
14 years 1 months ago
Concern based mining of heterogeneous software repositories
In the current trend of software engineering, software systems are viewed as clusters of overlapping structures representing various concerns, covering heterogeneous artifacts lik...
Imed Hammouda, Kai Koskimies
IJSI
2008
156views more  IJSI 2008»
13 years 7 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
KDD
2002
ACM
130views Data Mining» more  KDD 2002»
14 years 8 months ago
Learning domain-independent string transformation weights for high accuracy object identification
The task of object identification occurs when integrating information from multiple websites. The same data objects can exist in inconsistent text formats across sites, making it ...
Sheila Tejada, Craig A. Knoblock, Steven Minton
AAAI
1996
13 years 9 months ago
Post-Analysis of Learned Rules
Rule induction research implicitly assumes that after producing the rules from a dataset, these rules will be used directly by an expert system or a human user. In real-life appli...
Bing Liu, Wynne Hsu