45 search results - page 1 / 9 » Boosting in the Limit: Maximizing the Margin of Learned Ense... |
AAAI
1998
13 years 6 months ago
1998
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
IJCAI
2003
13 years 6 months ago
2003
In this paper we propose the framework of Monte Carlo algorithms as a useful one to analyze ensemble learning. In particular, this framework allows one to guess when bagging will ...
ICASSP
2011
IEEE
12 years 9 months ago
2011
IEEE
In online tracking, the tracker evolves to reflect variations in object appearance and surroundings. This updating process is formulated as a supervised learning problem, thus a ...
ALT
2006
Springer
14 years 2 months ago
2006
Springer
Abstract. We propose a thresholded ensemble model for ordinal regression problems. The model consists of a weighted ensemble of confidence functions and an ordered vector of thres...
ICML
2006
IEEE
14 years 6 months ago
2006
IEEE
We consider boosting algorithms that maintain a distribution over a set of examples. At each iteration a weak hypothesis is received and the distribution is updated. We motivate t...