9 search results - page 1 / 2 » Learning Kernel Perceptrons on Noisy Data Using Random Proje... |
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...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with n...
SBRN
2008
IEEE
13 years 11 months ago
2008
IEEE
The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which mus...
CVPR
2010
IEEE
13 years 7 months ago
2010
IEEE
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...
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 ...