Confidence bands for least squares support vector machine classifiers: A regression approach

摘要

This paper presents bias-corrected 100(1−α)% simultaneous confidence bands for least squares support vector machine classifiers based on a regression framework. The bias, which is inherently present in every nonparametric method, is estimated using double smoothing. In order to obtain simultaneous confidence bands we make use of the volume-of-tube formula. We also provide extensions of this formula in higher dimensions and show that the width of the bands are expanding with increasing dimensionality. Simulations and data analysis support its usefulness in practical real life classification problems.