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 and recognize patterns, used for classification and
regression analysis. The basic SVM takes a set of input data and
predicts, for each given input, which of two possible classes
forms the output, making it a non-probabilistic binary linear
classifier. Given a set of training examples, each marked as
belonging to one of two categories, an SVM training algorithm
builds a model that assigns new examples into one category or
the other. 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 which side of the gap
they fall on. In addition to performing linear classification,
SVMs can efficiently perform non-li