Density-induced margin support vector machines

Authors:
Li Zhang;Wei-Da Zhou
Affiliations:
Research Center of Machine Learning and Data Analysis, School of Computer Science and Technology, Soochow University, Suzhou 215006, China and Institute of Intelligent Information Processing, Xidi ...;Research Center of Machine Learning and Data Analysis, School of Computer Science and Technology, Soochow University, Suzhou 215006, China
Venue:
Pattern Recognition
Year:
2011

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Abstract

This paper proposes a new classifier called density-induced margin support vector machines (DMSVMs). DMSVMs belong to a family of SVM-like classifiers. Thus, DMSVMs inherit good properties from support vector machines (SVMs), e.g., unique and global solution, and sparse representation for the decision function. For a given data set, DMSVMs require to extract relative density degrees for all training data points. These density degrees can be taken as relative margins of corresponding training data points. Moreover, we propose a method for estimating relative density degrees by using the K nearest neighbor method. We also show the upper bound on the leave-out-one error of DMSVMs for a binary classification problem and prove it. Promising results are obtained on toy as well as real-world data sets.