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ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
13 years 7 months ago
Active Learning from Multiple Noisy Labelers with Varied Costs
In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, ...
Yaling Zheng, Stephen D. Scott, Kun Deng
ICDM
2010
IEEE
200views Data Mining» more  ICDM 2010»
13 years 7 months ago
Bayesian Maximum Margin Clustering
Abstract--Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered...
Bo Dai, Baogang Hu, Gang Niu
ICDM
2010
IEEE
198views Data Mining» more  ICDM 2010»
13 years 7 months ago
Hierarchical Ensemble Clustering
Ensemble clustering has emerged as an important elaboration of the classical clustering problems. Ensemble clustering refers to the situation in which a number of different (input)...
Li Zheng, Tao Li, Chris H. Q. Ding
ICDM
2010
IEEE
142views Data Mining» more  ICDM 2010»
13 years 7 months ago
Causal Discovery from Streaming Features
In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available ...
Kui Yu, Xindong Wu, Hao Wang, Wei Ding
ICDM
2010
IEEE
150views Data Mining» more  ICDM 2010»
13 years 7 months ago
Detecting Novel Discrepancies in Communication Networks
Abstract--We address the problem of detecting characteristic patterns in communication networks. We introduce a scalable approach based on set-system discrepancy. By implicitly lab...
James Abello, Tina Eliassi-Rad, Nishchal Devanur