A Case of Exponential Convergence Rates for SVM. (arXiv:2205.10055v1 [stat.ML])

Classification is often the first problem described in introductory machine
learning classes. Generalization guarantees of classification have historically
been offered by Vapnik-Chervonenkis theory. Yet those guarantees are based on
intractable algorithms, which has led to the theory of surrogate methods in
classification. Guarantees offered by surrogate methods are based on
calibration inequalities, which have been shown to be highly sub-optimal under
some margin conditions, failing short to capture exponential convergence
phenomena. Those "super" fast rates are becoming to be well …

arxiv case convergence ml svm