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PAMI
2011
13 years 2 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
ICML
2006
IEEE
14 years 8 months ago
A DC-programming algorithm for kernel selection
We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels fo...
Andreas Argyriou, Raphael Hauser, Charles A. Micch...
ICPR
2010
IEEE
14 years 2 months ago
Semi-Supervised Distance Metric Learning by Quadratic Programming
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original dist...
Hakan Cevikalp
CVPR
2011
IEEE
13 years 2 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
TNN
2010
234views Management» more  TNN 2010»
13 years 2 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes