Year: 2009
Type: conference-paper
Abstract: We present a new online boosting algorithm for updating the weights of a boosted classifier, which yields a closer approximation to the edges found by Freund and Schapire's AdaBoost algorithm than previous online boosting algorithms. We contribute a new way of deriving the online algorithm that ties together previous online boosting work. The online algorithm is derived by minimizing AdaBoost's loss as a single example is added to the training set. The equations show that the optimization is computationally expensive. However, a fast online approximation is possible. We compare approximation error to edges found by batch AdaBoost on synthetic datasets and generalization error on face datasets and the MNIST dataset.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2094047042