What do the support vectors mean in kernel based svm? are they the vectors that are of distance 1 from the hyperplane that linearly separates the data in the higher dimensional space? Thank you in advance
2026-03-26 17:52:10.1774547530
Support vectors in kernel based SVM
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As in the linear case, they are vectors on the boundary. Below I annotated one example of the iris dataset, classified with a Radial Basis Function (RBF) kernel