title: 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 paper, 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.
authors.affiliation: Department of Statistics, Temple University, Philadelphia, PA 19122 ( yichao.wu@temple.edu ).
authors.forename: Yichao
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Statistics and Operations Research, Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC 27599 ( yfliu@email.unc.edu ).
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Liu
keywords: Binary classification; SVM; data adaptive learning; large margin; multicategory classification; weighted learning