Support vector machine use mathematic model to segregate the dimension into 2 classes. The data points of a class is congregated on 1 side of the model (line, plane, multi dimensional plane). Tye distance between the separation plane and the closest vectors form the margin. The vectors on the margin are called support vectors. SVM is the methodology to find the optimal separation plane.
The advantage of SVM is it requires less computing power than neural network.
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