Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning

The theoretical elegance of Support Vector Machines (SVMs), grounded in statistical learning theory, establishes them as a reference for robust classification. However, in the era of Big Data, classical SVMs face two major bottlenecks: the prohibitive cubic complexity $\mathcal{O}(N^3)$ of training