Support Vector Machine

Support Vector Machine

SVMs are discriminative classifier defined by a separating hyperplane. Given labeled training data, the algorithm will find the optimal output to categorize the inputs.The tuning parameters include kernel, regularization, and gamma. Kernels do transformations and calculate separation lines, regularisation tells the algorithm the extent of misclassifying inputs, and the extent of how much input data is considered.

See Savan Patel’s explanation of SVM on Medium

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