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» Invariances in kernel methods: From samples to objects
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CVPR
2008
IEEE
14 years 11 months ago
Margin-based discriminant dimensionality reduction for visual recognition
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
Hakan Cevikalp, Bill Triggs, Frédéri...
APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
14 years 2 months ago
Sampling Bounds for Stochastic Optimization
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Moses Charikar, Chandra Chekuri, Martin Pál
DAC
1996
ACM
14 years 1 months ago
Fast Parameters Extraction of General Three-Dimension Interconnects Using Geometry Independent Measured Equation of Invariance
Measured Equation of Invariance(MEI) is a new concept in computational electromagnetics. It has been demonstrated that the MEI technique can be used to terminate the meshes very c...
Weikai Sun, Wayne Wei-Ming Dai, Wei Hong II
JMLR
2002
137views more  JMLR 2002»
13 years 8 months ago
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Masashi Sugiyama, Klaus-Robert Müller
ICASSP
2011
IEEE
13 years 21 days ago
Explicit recursivity into reproducing kernel Hilbert spaces
This paper presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces (RKHS). Unlike previous approaches that exploit the kernel trick on filtered ...
Devis Tuia, Gustavo Camps-Valls, Manel Martí...