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» A Partial-Repeatability Approach to Data Mining
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ICDM
2005
IEEE
185views Data Mining» more  ICDM 2005»
15 years 3 months ago
Semi-Supervised Mixture of Kernels via LPBoost Methods
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based o...
Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao
80
Voted
PAKDD
2004
ACM
143views Data Mining» more  PAKDD 2004»
15 years 3 months ago
Compact Dual Ensembles for Active Learning
Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how...
Amit Mandvikar, Huan Liu, Hiroshi Motoda
81
Voted
ICDM
2010
IEEE
134views Data Mining» more  ICDM 2010»
14 years 8 months ago
Consequences of Variability in Classifier Performance Estimates
The prevailing approach to evaluating classifiers in the machine learning community involves comparing the performance of several algorithms over a series of usually unrelated data...
Troy Raeder, T. Ryan Hoens, Nitesh V. Chawla
95
Voted
KAIS
2008
119views more  KAIS 2008»
14 years 10 months ago
An information-theoretic approach to quantitative association rule mining
Abstract. Quantitative Association Rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and th...
Yiping Ke, James Cheng, Wilfred Ng
78
Voted
SDM
2007
SIAM
140views Data Mining» more  SDM 2007»
14 years 11 months ago
A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions
In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but...
Jing Gao, Wei Fan, Jiawei Han, Philip S. Yu