176 search results - page 14 / 36 » Learning with Transformation Invariant Kernels |
CVPR
2006
IEEE
14 years 9 months ago
2006
IEEE
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...
CVPR
2009
IEEE
15 years 2 months ago
2009
IEEE
Several recently-proposed architectures for highperformance
object recognition are composed of two main
stages: a feature extraction stage that extracts locallyinvariant
feature...
ACCV
1998
Springer
13 years 11 months ago
1998
Springer
This paper describes a method for recognizing partially occluded objects under different levels of illumination brightness by using the eigenspace analysis. In our previous work, w...
IJCAI
2007
13 years 8 months ago
2007
A key challenge in applying kernel-based methods for discriminative learning is to identify a suitable kernel given a problem domain. Many methods instead transform the input data...
PKDD
2010
Springer
13 years 5 months ago
2010
Springer
Abstract. The eigenspectrum of a graph Laplacian encodes smoothness information over the graph. A natural approach to learning involves transforming the spectrum of a graph Laplaci...