Online Coordinate Boosting

Details Online Coordinate Boosting Online Coordinate Boosting

2008-10-24

arxiv.org/abs/0810.4553v1

Statistics

Machine Learning

9 pages, 4 figures

Scientific paper

We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire's AdaBoost algorithm than previous online boosting algorithms. We also contribute a new way of deriving the online algorithm that ties together previous online boosting work. We assume that the weak hypotheses were selected beforehand, and only their weights are updated during online boosting. The update rule is derived by minimizing AdaBoost's loss when viewed in an incremental form. The equations show that optimization is computationally expensive. However, a fast online approximation is possible. We compare approximation error to batch AdaBoost on synthetic datasets and generalization error on face datasets and the MNIST dataset.

Affiliated with

Also associated with

No associations

Rating

Online Coordinate Boosting does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Online Coordinate Boosting, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Online Coordinate Boosting will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-668866