Support-vector machine [Neurosurgery Wiki]

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====== Support-vector machine ======
In [[machine learning]], support-vector machines (SVMs, also support-vector networks) are [[supervised learning]] models with associated learning algorithms that analyze data used for classification and [[regression analysis]]. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall.