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https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
https://www.metacademy.org/graphs/concepts/support_vector_machine
The support vector machine (SVM) is a classification algorithm which tries to fit a hyperplane which maximizes the margin, or the smallest distance separating an example from the decision boundary. The main advantage is that SVMs can be kernelized, allowing them …
https://towardsdatascience.com/understanding-support-vector-machine-part-1-lagrange-multipliers-5c24a52ffc5e
Nov 24, 2018 · Mathematics of Support Vector Machine: If you have forgotten the problem statement, let me remind you once again. In figure 1, we are to find a line that best separates two samples. We consider a vector (W) perpendicular to the median line (red line) and, an unknown sample which can be represented by vector x.Author: Saptashwa Bhattacharyya
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