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KDD
2009
ACM
224views Data Mining» more  KDD 2009»
14 years 1 months ago
Issues in evaluation of stream learning algorithms
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
João Gama, Raquel Sebastião, Pedro P...
DATAMINE
2006
130views more  DATAMINE 2006»
13 years 8 months ago
Mining Adaptive Ratio Rules from Distributed Data Sources
Different from traditional association-rule mining, a new paradigm called Ratio Rule (RR) was proposed recently. Ratio rules are aimed at capturing the quantitative association kno...
Jun Yan, Ning Liu, Qiang Yang, Benyu Zhang, QianSh...
KDD
2010
ACM
259views Data Mining» more  KDD 2010»
14 years 22 days ago
A probabilistic model for personalized tag prediction
Social tagging systems have become increasingly popular for sharing and organizing web resources. Tag recommendation is a common feature of social tagging systems. Social tagging ...
Dawei Yin, Zhenzhen Xue, Liangjie Hong, Brian D. D...
MICCAI
2005
Springer
14 years 9 months ago
Efficient Learning by Combining Confidence-Rated Classifiers to Incorporate Unlabeled Medical Data
Abstract. In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabel...
Weijun He, Xiaolei Huang, Dimitris N. Metaxas, Xia...
SDM
2012
SIAM
322views Data Mining» more  SDM 2012»
11 years 11 months ago
Adaptive Multi-task Sparse Learning with an Application to fMRI Study
In this paper, we consider the multi-task sparse learning problem under the assumption that the dimensionality diverges with the sample size. The traditional l1/l2 multi-task lass...
Xi Chen, Jingrui He, Rick Lawrence, Jaime G. Carbo...