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GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
15 years 10 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
CVPR
2010
IEEE
15 years 6 months ago
Learning kernels for variants of normalized cuts: Convex relaxations and applications
We propose a new algorithm for learning kernels for variants of the Normalized Cuts (NCuts) objective – i.e., given a set of training examples with known partitions, how should ...
Lopamudra Mukherjee, Vikas Singh, Jiming Peng, Chr...
PR
2006
102views more  PR 2006»
15 years 6 months ago
Prototype selection for dissimilarity-based classifiers
A conventional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. A more efficient and sometimes a more accurate solution is offere...
Elzbieta Pekalska, Robert P. W. Duin, Pavel Pacl&i...
PR
2007
133views more  PR 2007»
15 years 6 months ago
Incorporating multiple SVMs for automatic image annotation
In this paper, a novel automatic image annotation system is proposed, which integrates two sets of support vector machines (SVMs), namely the multiple instance learning (MIL)-base...
Xiaojun Qi, Yutao Han
CVPR
2012
IEEE
13 years 9 months ago
Meta-class features for large-scale object categorization on a budget
In this paper we introduce a novel image descriptor enabling accurate object categorization even with linear models. Akin to the popular attribute descriptors, our feature vector ...
Alessandro Bergamo, Lorenzo Torresani