99 search results - page 7 / 20 » Feature Selection by Transfer Learning with Linear Regulariz... |
NIPS
2001
13 years 9 months ago
2001
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
ESANN
2006
13 years 9 months ago
2006
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
SDM
2008
SIAM
13 years 9 months ago
2008
SIAM
With the advent of high throughput technologies, feature selection has become increasingly important in a wide range of scientific disciplines. We propose a new feature selection ...
JMLR
2011
13 years 2 months ago
2011
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...
ICCV
2007
IEEE
14 years 9 months ago
2007
IEEE
An approach for fast tracking of arbitrary image features with no prior model and no offline learning stage is presented. Fast tracking is achieved using banks of linear displacem...