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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.

SUBMITTER: Wu Y

PROVIDER: S-EPMC3867158 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Adaptively Weighted Large Margin Classifiers.

Wu Yichao Y Liu Yufeng Y

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20130101 2

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 cla ...[more]

PMID: 24363545

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