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ICML
2005
IEEE
14 years 11 months ago
Weighted decomposition kernels
We introduce a family of kernels on discrete data structures within the general class of decomposition kernels. A weighted decomposition kernel (WDK) is computed by dividing objec...
Sauro Menchetti, Fabrizio Costa, Paolo Frasconi
PAMI
2010
337views more  PAMI 2010»
13 years 8 months ago
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
—This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based ...
Kwang In Kim, Younghee Kwon
ALT
2004
Springer
14 years 7 months ago
On Kernels, Margins, and Low-Dimensional Mappings
Kernel functions are typically viewed as providing an implicit mapping of points into a high-dimensional space, with the ability to gain much of the power of that space without inc...
Maria-Florina Balcan, Avrim Blum, Santosh Vempala
CORR
2012
Springer
171views Education» more  CORR 2012»
12 years 5 months ago
Random Feature Maps for Dot Product Kernels
Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and...
Purushottam Kar, Harish Karnick
ICCV
2009
IEEE
13 years 7 months ago
Kernel map compression using generalized radial basis functions
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
Omar Arif, Patricio A. Vela