On the Noise Model of Support Vector Machines Regression
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Advanced lectures on machine learning
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Least squares estimators are very common in statistics, but they lead to results that are very sensitive to outliers, and it has been proposed to minimize other measures of error, that lead to ``robust'''' estimates. In this paper we show that using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the estimator.