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TIP
2010
155views more  TIP 2010»
13 years 6 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
STOC
1991
ACM
84views Algorithms» more  STOC 1991»
13 years 11 months ago
Self-Testing/Correcting for Polynomials and for Approximate Functions
The study of self-testing/correcting programs was introduced in [8] in order to allow one to use program P to compute function f without trusting that P works correctly. A self-te...
Peter Gemmell, Richard J. Lipton, Ronitt Rubinfeld...
ECML
2007
Springer
14 years 1 months ago
Dual Strategy Active Learning
Abstract. Active Learning methods rely on static strategies for sampling unlabeled point(s). These strategies range from uncertainty sampling and density estimation to multi-factor...
Pinar Donmez, Jaime G. Carbonell, Paul N. Bennett
ICONIP
2009
13 years 5 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
TIP
2008
98views more  TIP 2008»
13 years 7 months ago
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation
The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a...
Xiangjun Zhang, Xiaolin Wu