159 search results - page 4 / 32 » Boosting margin based distance functions for clustering |
NIPS
2004
13 years 7 months ago
2004
A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf distance. The Laplacian pdf distance exhibits a remarkabl...
CVPR
2004
IEEE
14 years 8 months ago
2004
IEEE
Model-based clustering of motion trajectories can be posed as the problem of learning an underlying mixture density function whose components correspond to motion classes with dif...
NIPS
2007
13 years 7 months ago
2007
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended...
ICPR
2006
IEEE
14 years 7 months ago
2006
IEEE
Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance f...
TKDE
2012
11 years 8 months ago
2012
—The pairwise constraints specifying whether a pair of samples should be grouped together or not have been successfully incorporated into the conventional clustering methods such...