13 search results - page 2 / 3 » Learning and approximation by Gaussians on Riemannian manifo... |
MM
2004
ACM
13 years 10 months ago
2004
ACM
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...
ICML
2005
IEEE
14 years 6 months ago
2005
IEEE
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis function...
IJCV
2007
13 years 5 months ago
2007
The parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parame...
ICCV
2007
IEEE
13 years 11 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
COLT
1994
Springer
13 years 9 months ago
1994
Springer
We examine the relationship between the VCdimension and the number of parameters of a smoothly parametrized function class. We show that the VC-dimension of such a function class ...