Article ID Journal Published Year Pages File Type
6941380 Pattern Recognition Letters 2014 10 Pages PDF
Abstract
In this theoretical work we approach the class of relative margin classification algorithms from the mathematical programming perspective. In particular, we propose a Balanced Relative Margin Machine (BRMM) and then extend it by a 1-norm regularization. We show that this new classifier concept connects Support Vector Machines (SVM) with Fisher's Discriminant Analysis (FDA) by the insertion of a range parameter. It is also strongly connected to the Support Vector Regression. Using this BRMM it is now possible to optimize the classifier type instead of choosing it beforehand. We verify our findings empirically by means of simulated and benchmark data.
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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