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ICDM
2009
IEEE
199views Data Mining» more  ICDM 2009»
15 years 11 months ago
Active Learning with Adaptive Heterogeneous Ensembles
—One common approach to active learning is to iteratively train a single classifier by choosing data points based on its uncertainty, but it is nontrivial to design uncertainty ...
Zhenyu Lu, Xindong Wu, Josh Bongard
PKDD
2009
Springer
153views Data Mining» more  PKDD 2009»
15 years 11 months ago
Subspace Regularization: A New Semi-supervised Learning Method
Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the same label. This assumption, ...
Yan-Ming Zhang, Xinwen Hou, Shiming Xiang, Cheng-L...
ICDM
2009
IEEE
92views Data Mining» more  ICDM 2009»
15 years 2 months ago
Semi-supervised Multi-task Learning with Task Regularizations
Multi-task learning refers to the learning problem of performing inference by jointly considering multiple related tasks. There have already been many research efforts on supervise...
Fei Wang, Xin Wang, Tao Li
ICML
2010
IEEE
15 years 2 months ago
Implicit Online Learning
Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data ana...
Brian Kulis, Peter L. Bartlett
KDD
2003
ACM
175views Data Mining» more  KDD 2003»
16 years 4 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...