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» Phenomenal Data Mining: From Data to Phenomena
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ICDM
2009
IEEE
109views Data Mining» more  ICDM 2009»
15 years 8 months ago
Semi-naive Exploitation of One-Dependence Estimators
—It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensu...
Nan Li, Yang Yu, Zhi-Hua Zhou
ICDM
2009
IEEE
117views Data Mining» more  ICDM 2009»
15 years 8 months ago
Discriminative Mixed-Membership Models
Although mixed-membership models have achieved great success in unsupervised learning, they have not been widely applied to classification problems. In this paper, we propose a f...
Hanhuai Shan, Arindam Banerjee, Nikunj C. Oza
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ICDM
2009
IEEE
113views Data Mining» more  ICDM 2009»
15 years 8 months ago
Connecting Sparsely Distributed Similar Bloggers
—The nature of the Blogosphere determines that the majority of bloggers are only connected with a small number of fellow bloggers, and similar bloggers can be largely disconnecte...
Nitin Agarwal, Huan Liu, Shankara B. Subramanya, J...
PKDD
2009
Springer
144views Data Mining» more  PKDD 2009»
15 years 8 months ago
Evaluation Measures for Multi-class Subgroup Discovery
Subgroup discovery aims at finding subsets of a population whose class distribution is significantly different from the overall distribution. It has previously predominantly been...
Tarek Abudawood, Peter Flach
PKDD
2009
Springer
134views Data Mining» more  PKDD 2009»
15 years 8 months ago
Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)
Abstract. We address the problem of joint feature selection in multiple related classification or regression tasks. When doing feature selection with multiple tasks, usually one c...
Paramveer S. Dhillon, Brian Tomasik, Dean P. Foste...