Interval discriminant analysis using Support Vector Machines
Cecilio Ángulo, Davide Anguita, Luis González Abril · 2007
Abstract. Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval–represented data. Most approaches for SVM learning of interval data use local kernels based on interval distances. We present here a novel approach, suitable for linear SVMs, which allows to deal with interval data without resorting to interval distances. The experimental results confirms the validity of our proposal. 1