Support Vector Classification with Input Data Uncertainty

A novel formulation of support vector classification is proposed, which allows uncertainty in input data, and an intuitive geometric interpretation is derived of the proposed formulation, and algorithms to efficiently solve it are developed.

Abstract

This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the statistical reasoning, we propose a novel formulation of support vector classification, which allows uncertainty in input data. We derive an intuitive geometric interpretation of the proposed formulation, and develop algorithms to efficiently solve it. Empirical results are included to show that the newly formed method is superior to the standard SVM for problems with noisy input. 1