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As you mention, storing the kernel matrix requires memory that scales quadratically with the number of data points. Training time for traditional SVM algorithms also scales superlinearly with the number of data points. So, these algorithms aren't feasible for large data sets. One possible trick is to reformulate a kernelized SVM as a linear SVM.

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