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IJSI
2008
156views more  IJSI 2008»
13 years 8 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
CORR
2008
Springer
115views Education» more  CORR 2008»
13 years 9 months ago
New probabilistic interest measures for association rules
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for ...
Michael Hahsler, Kurt Hornik
KDD
2008
ACM
213views Data Mining» more  KDD 2008»
14 years 9 months ago
Heterogeneous data fusion for alzheimer's disease study
Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive a...
Jieping Ye, Kewei Chen, Teresa Wu, Jing Li, Zheng ...
EDBT
2012
ACM
257views Database» more  EDBT 2012»
11 years 11 months ago
Indexing and mining topological patterns for drug discovery
Increased availability of large repositories of chemical compounds has created new challenges and opportunities for the application of data-mining and indexing techniques to probl...
Sayan Ranu, Ambuj K. Singh
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 10 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider