105 search results - page 2 / 21 » Invariant kernel functions for pattern analysis and machine ... |
NIPS
2007
13 years 6 months ago
2007
This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation inv...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Som...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We pr...
ICCV
2009
IEEE
13 years 2 months ago
2009
IEEE
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...
NIPS
2007
13 years 6 months ago
2007
Incorporating invariances into a learning algorithm is a common problem in machine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbit...