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in machine learning one of the most fundamental tasks is when you have a bunch of objects that you want to classify into two categories or more is this picture of a dog or cat is this stock going up or down svms or support vector machines are some of the simplest and arguably the most elegant methods for classification each object you want to classify is represented as a point in an n-dimensional space and the coordinates of this point are usually called features svms perform the classification test by drawing a hyperplane that is a line in 2d or a plane in 3d in such a way that all points of one category are on one side of the hyperplane and all points of the other category are on the other side and while there could be multiple such hyperplanes svm tries to find the one that best separates the two categories in the sense that it maximizes the distance to points in either category this distance is called the margin and the points that fall exactly on the margin are called the supporti
