101 search results - page 1 / 21 » Learning to Cluster Using High Order Graphical Models with L... |
ICCV
2011
IEEE
12 years 4 months ago
2011
IEEE
This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem w...
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...
DAGM
2010
Springer
13 years 5 months ago
2010
Springer
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
CSDA
2010
13 years 4 months ago
2010
We propose a model for a point-referenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially co...