What is the role of scaling SVM to large datasets using linear approximations?

Linear approximations make SVM scalable by avoiding kernel computations.

Linear SVM avoids kernel matrix computation and uses optimization methods like coordinate descent or SGD. This reduces complexity from quadratic to linear in number of samples, making it suitable for large datasets.