Figure 2
From: Nanopore Detector based analysis of single-molecule conformational kinetics and binding interactions
A sketch of the hyperplane separability heuristic for SVM binary classification (from [8]). An SVM is trained to find an optimal hyperplane that separates positive and negative instances, while also constrained by structural risk minimization (SRM) criteria, which here manifests as the hyperplane having a thickness, or "margin," that is made as large as possible in seeking a separating hyperplane. A benefit of using SRM is much less complication due to overfitting (a problem with Neural Network discrimination approaches).