A modified SVM classification algorithm for data of variable quality

We propose a modified SVM algorithm for the classification of data augmented with explicit quality quantification for each example in the training set. As the extension to nonlinear decision functions through the use of kernels brings to a non-convex optimization problem, we develop an approximate solution. Finally, the proposed approach is applied to a set of benchmarks and contrasted with analogous methodologies in the literature.