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» Learning to Select a Ranking Function
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CVPR
2009
IEEE
15 years 3 months ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
CAGD
2005
116views more  CAGD 2005»
13 years 8 months ago
A sharpness dependent filter for mesh smoothing
In this paper, we propose a sharpness dependent filter design based on the fairing of surface normal, whereby the filtering algorithm automatically selects a filter. This may be a...
Chun-Yen Chen, Kuo-Young Cheng
CGO
2010
IEEE
14 years 3 months ago
Automatic creation of tile size selection models
Tiling is a widely used loop transformation for exposing/exploiting parallelism and data locality. Effective use of tiling requires selection and tuning of the tile sizes. This is...
Tomofumi Yuki, Lakshminarayanan Renganarayanan, Sa...
ICML
2003
IEEE
14 years 9 months ago
Incorporating Diversity in Active Learning with Support Vector Machines
In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
Klaus Brinker
ICML
1997
IEEE
14 years 9 months ago
Characterizing the generalization performance of model selection strategies
Abstract: We investigate the structure of model selection problems via the bias/variance decomposition. In particular, we characterize the essential structure of a model selection ...
Dale Schuurmans, Lyle H. Ungar, Dean P. Foster