59 search results - page 4 / 12 » Learning Kernel Expansions for Image Classification |
ECCV
2010
Springer
14 years 3 hour ago
2010
Springer
Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear sim...
ICIP
2010
IEEE
13 years 5 months ago
2010
IEEE
Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vecto...
DAGM
2010
Springer
13 years 8 months ago
2010
Springer
Abstract. Approximations based on random Fourier features have recently emerged as an efficient and elegant methodology for designing large-scale kernel machines [4]. By expressing...
CVPR
2003
IEEE
14 years 9 months ago
2003
IEEE
We consider the problem of learning with instances defined over a space of sets of vectors. We derive a new positive definite kernel f(A B) defined over pairs of matrices A B base...
CVPR
2010
IEEE
13 years 11 months ago
2010
IEEE
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...