3 search results - page 1 / 1 » Random Projection, Margins, Kernels, and Feature-Selection |
SLSFS
2005
Springer
13 years 10 months ago
2005
Springer
Random projection is a simple technique that has had a number of applications in algorithm design. In the context of machine learning, it can provide insight into questions such as...
ESANN
2006
13 years 5 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 ...
ALT
2007
Springer
14 years 1 months ago
2007
Springer
In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classific...