Question: Why support vector machine (SVM) scales up the performance compared to linear classifiers? What are the regularization parameters for SVM? What is the primary motivation

Why support vector machine (SVM) scales up the performance compared to linear classifiers? What are the regularization parameters for SVM? What is the primary motivation for using the kernel trick in machine learning algorithms? For linearly separable data, can a small slack penalty (C) hurt the training accuracy when using a linear SVM (no kernel)? If so, explain how. If not, why not?

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