30 search results - page 2 / 6 » The Most Robust Loss Function for Boosting |
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
Boosting is a popular approach for building accurate classifiers. Despite the initial popular belief, boosting algorithms do exhibit overfitting and are sensitive to label noise. ...
ECCV
2010
Springer
13 years 11 months ago
2010
Springer
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
NIPS
2004
13 years 7 months ago
2004
We study a method of optimal data-driven aggregation of classifiers in a convex combination and establish tight upper bounds on its excess risk with respect to a convex loss funct...
IJCNN
2007
IEEE
14 years 11 days ago
2007
IEEE
—The 0/1 loss is an important cost function for perceptrons. Nevertheless it cannot be easily minimized by most existing perceptron learning algorithms. In this paper, we propose...
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
The performance of graph based clustering methods critically depends on the quality of the distance function, used to compute similarities between pairs of neighboring nodes. In t...