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» When Semi-supervised Learning Meets Ensemble Learning
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KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 7 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
NIPS
2001
13 years 8 months ago
Global Coordination of Local Linear Models
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinto...
SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
13 years 9 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...
CVPR
2008
IEEE
14 years 9 months ago
Least squares congealing for unsupervised alignment of images
In this paper, we present an approach we refer to as "least squares congealing" which provides a solution to the problem of aligning an ensemble of images in an unsuperv...
Mark Cox, Sridha Sridharan, Simon Lucey, Jeffrey F...
ICASSP
2011
IEEE
12 years 11 months ago
Increasing discriminative capability on MAP-based mapping function estimation for acoustic model adaptation
In this study, we propose increasing discriminative power on the maximum a posteriori (MAP)-based mapping function estimation for acoustic model adaptation. Based on the effective...
Yu Tsao, Ryosuke Isotani, Hisashi Kawai, Satoshi N...