This paper describes a new large margin classifier, named SVM/LDA. This classifier can be viewed as an extension of support vector machine (SVM) by incorporating some global information about the data. The SVM/LDA classifier can be also seen as a generalization of linear discriminant analysis (LDA) by incorporating the idea of (local) margin maximization into standard LDA formulation. We show that existing SVM software can be used to solve the SVM/LDA formulation. We also present empirical comparisons of the proposed algorithm with SVM and LDA using both synthetic and real world benchmark data.
|Original language||English (US)|
|Number of pages||5|
|State||Published - 2005|
|Event||International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada|
Duration: Jul 31 2005 → Aug 4 2005
|Other||International Joint Conference on Neural Networks, IJCNN 2005|
|Period||7/31/05 → 8/4/05|
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