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» TRUST-TECH based Methods for Optimization and Learning
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SIGIR
2006
ACM
15 years 6 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
CCECE
2009
IEEE
15 years 7 months ago
Inverse data transformation for change detection in wind turbine diagnostics
A complex system is expected to show different nominal behaviors under different conditions, and the deviation over time from these nominal behaviors is an indicator of potential ...
Yanjun Yan, Lisa Ann Osadciw, Glen Benson, Eric Wh...
104
Voted
VLDB
2007
ACM
137views Database» more  VLDB 2007»
15 years 6 months ago
Detecting Attribute Dependencies from Query Feedback
Real-world datasets exhibit a complex dependency structure among the data attributes. Learning this structure is a key task in automatic statistics configuration for query optimi...
Peter J. Haas, Fabian Hueske, Volker Markl
84
Voted
CVPR
2007
IEEE
16 years 2 months ago
Quality-Driven Face Occlusion Detection and Recovery
This paper presents a framework to automatically detect and recover the occluded facial region. We first derive a Bayesian formulation unifying the occlusion detection and recover...
Dahua Lin, Xiaoou Tang
CVPR
2007
IEEE
16 years 2 months ago
A Multi-Scale Tikhonov Regularization Scheme for Implicit Surface Modelling
Kernel machines have recently been considered as a promising solution for implicit surface modelling. A key challenge of machine learning solutions is how to fit implicit shape mo...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu