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ICML
2000
IEEE
16 years 7 months ago
Discovering Homogeneous Regions in Spatial Data through Competition
If all features causing heterogeneity were observed, a mixture of experts approach (Jacobs et al., 1991) is likely to be superior to using a single model. When unobserved or very n...
Slobodan Vucetic, Zoran Obradovic
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
16 years 7 months ago
Effective multi-label active learning for text classification
Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical ...
Bishan Yang, Jian-Tao Sun, Tengjiao Wang, Zheng Ch...
KDD
2008
ACM
232views Data Mining» more  KDD 2008»
16 years 7 months ago
Anticipating annotations and emerging trends in biomedical literature
The BioJournalMonitor is a decision support system for the analysis of trends and topics in the biomedical literature. Its main goal is to identify potential diagnostic and therap...
Bernd Wachmann, Dmitriy Fradkin, Fabian Mörch...
174
Voted
KDD
2008
ACM
140views Data Mining» more  KDD 2008»
16 years 7 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
215
Voted
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
16 years 7 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto