In this paper we establish mistake bounds with moving targets for general linear classification algorithms. We have bounds for two algorithms, a simplified.
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We consider using online large margin classification algorithms in a setting where the target classifier may change over time. The algorithms we consider ...
Nov 8, 2002 · We consider using online large margin classification algorithms in a setting where the target classifier may change over time.
We consider using online large margin classification algorithms in a setting where the target classifier may change over time.
Original language, English. Title of host publication, Large margin classification for moving targets. Editors, Cesa-Bianchi, Masayuki Numao Rudiger ...
We consider using online large margin classification algorithms in a setting where the target classifier may change over time. The algorithms we consider ...
Jan 6, 2020 · The goal is to have the largest possible margin between the decision boundary that separates the two classes and the training instances.
The two most popular large margin classification algorithms are SVM and Boosting. Both of them have been widely used in solving different kinds of computer ...
Aug 15, 2023 · This paper introduces margin distribution into multi-class supervised novelty detection and proposes a large margin distribution-supervised novelty detection ( ...
In this dissertation, we address the problem of adversarial machine learning;. i.e., our goal is to build safe machine learning algorithms that are robust in ...