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2) A lot of people have worked on making SVMs scalable. But you have to realize that scikit learn is a library that only offers basic versions of basic algorithms, so if you think you'll find advanced approaches to SVM scalability there you'll end up disappointed. But there are versions that use GPUs or a multithreaded implementation for the computation of the kernel matrices (which is the most expensive part of training). The "solver" (=optimizer) part of an SVM can also be scaled. scikit-learn relies on libsvm for its solver, which by default is single threaded. But nothing stops you from applying other solvers (e.g. Gradient Descent, with all of it's niceties) to the problem. I'm not sure what's the state of the art today, but it used to be that e.g. vowpal wabbit had a bunch of really fast, scalable SVM algorithms.
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