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NIPS
2008
15 years 1 months ago
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depe...
Francis Bach
JMLR
2008
131views more  JMLR 2008»
14 years 11 months ago
On Relevant Dimensions in Kernel Feature Spaces
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert M...
CP
2007
Springer
15 years 6 months ago
A Generic Geometrical Constraint Kernel in Space and Time for Handling Polymorphic k-Dimensional Objects
: This report introduces a geometrical constraint kernel for handling the location in space and time of polymorphic k-dimensional objects subject to various geometrical and time co...
Nicolas Beldiceanu, Mats Carlsson, Emmanuel Poder,...
BMCBI
2007
154views more  BMCBI 2007»
14 years 12 months ago
Inferring biological networks with output kernel trees
Background: Elucidating biological networks between proteins appears nowadays as one of the most important challenges in systems biology. Computational approaches to this problem ...
Pierre Geurts, Nizar Touleimat, Marie Dutreix, Flo...
ICIP
2009
IEEE
16 years 27 days ago
Optimum Kernel Function Design From Scale Space Features For Object Detection
Scale-space representation of an image is a significant way to generate features for classification. However, for a specific classification task, the entire scale-space may not be...