569 search results - page 63 / 114 » Online Bounds for Bayesian Algorithms |
SIROCCO
2007
14 years 11 months ago
2007
Abstract. A large group of autonomous, mobile entities e.g. robots initially placed at some arbitrary node of the graph has to jointly visit all nodes (not necessarily all edges) a...
ICML
1994
IEEE
15 years 1 months ago
1994
IEEE
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected...
WAOA
2004
Springer
15 years 3 months ago
2004
Springer
We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize the weighted completion times of jobs. In contrast to the classical stochastic ...
COLT
2006
Springer
15 years 1 months ago
2006
Springer
In this paper, we study a sequential decision making problem. The objective is to maximize the total reward while satisfying constraints, which are defined at every time step. The...
PKDD
2010
Springer
14 years 8 months ago
2010
Springer
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and the...