98 search results - page 9 / 20 » On the Value of Objective Function Adaptation in Online Opti... |
ICPR
2010
IEEE
14 years 1 months ago
2010
IEEE
—Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in...
GECCO
2004
Springer
14 years 26 days ago
2004
Springer
Abstract. The usual approach to deal with noise present in many realworld optimization problems is to take an arbitrary number of samples of the objective function and use the samp...
COLT
2006
Springer
13 years 11 months ago
2006
Springer
We consider online learning in repeated decision problems, within the framework of a repeated game against an arbitrary opponent. For repeated matrix games, well known results esta...
CVIU
2004
13 years 7 months ago
2004
This study presents a content-based image retrieval system IMALBUM based on local region of interest called object of interest (OOI). Each segmented or user-selected OOI is indexe...
APPROX
2000
Springer
13 years 11 months ago
2000
Springer
In this paper, we derive bounds on performance guarantees of online algorithms for real-time preemptive scheduling of jobs with deadlines on K machines when jobs are characterized ...