Adaptively Weighted Large Margin Classifiers

Abstract

Large margin classifiers have been shown to be very useful in many applications. The support vector machine is a canonical example of large margin classifiers. Despite their flexibility and ability in handling high-dimensional data, many large margin classifiers have serious drawbacks when the data are noisy, especially when there are outliers in the data. In this article, we propose a new weighted large margin classification technique. The weights are chosen adaptively with data. The proposed classifiers are shown to be robust to outliers and thus are able to produce more accurate classification results.