22 search results - page 3 / 5 » Worst-Case Absolute Loss Bounds for Linear Learning Algorith... |
SIAMJO
2010
13 years 14 days ago
2010
We study the problem of minimizing the expected loss of a linear predictor while constraining its sparsity, i.e., bounding the number of features used by the predictor. While the r...
NIPS
2001
13 years 7 months ago
2001
In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of hypotheses generated by an arbitrary on-line learning algorithm run on an independent an...
COLT
2001
Springer
13 years 10 months ago
2001
Springer
Abstract. We develop three new techniques to build on the recent advances in online learning with kernels. First, we show that an exponential speed-up in prediction time per trial ...
COLT
2008
Springer
13 years 7 months ago
2008
Springer
Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that ...
CN
2008
13 years 5 months ago
2008
This paper deals with a congestion control framework for elastic and real-time traffic, where the user's application is associated with a utility function. We allow users to ...