625 search results - page 63 / 125 » Large-scale manifold learning |
DAGM
2008
Springer
13 years 11 months ago
2008
Springer
In this paper a novel and generic approach for model-based data clustering in a boosting framework is presented. This method uses the forward stagewise additive modeling to learn t...
IJCAI
2007
13 years 11 months ago
2007
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
AAAI
2007
14 years 9 days ago
2007
Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing. We consider the case where data is sampled from a low d...
ICMCS
2005
IEEE
14 years 3 months ago
2005
IEEE
For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. A major obstacle to this is the insufficiency o...
NIPS
2003
13 years 11 months ago
2003
Recent research has demonstrated that useful POMDP solutions do not require consideration of the entire belief space. We extend this idea with the notion of temporal abstraction. ...