In this paper, a novel algorithm, SV-Means, is formulated for open set radar waveform classification. The approach extends the quantile one-class support vector machine (q-OCSVM) density estimation algorithm into a classification formulation with inspiration from k-means and stochastic gradient descent principles. Phase-modulated radar waveform data is used to compare the primal and dual q-OCSVM (configured for classification) to SV-Means to verify the algorithm's classification and rejection performance. SV-Means is also shown to be an effective open set classification algorithm and is compared to these other open set classifiers: 1-vs-set machine, W-SVM, PI-SVM, and probabilistic open space (POS)-SVM. The SV-Means algorithm proves to achieve similar, and some cases better, performance against comparable algorithms in a fraction of the training time (as the data records increase and, in the case of the q-OCSVM, level sets increase). The SV-Means open set algorithm is an attractive adaptive classification framework using any feature extraction approach including deep-learning architectures.