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Support Vector Machine (SVM) was first heard in 1992,
introduced by Boser, Guyon, and Vapnik in COLT-92. Support vector machines (SVMs) are a set of related supervised learning methods used for classification and
regression. They belong to a family of generalized linear classifiers. In
another terms, Support Vector Machine (SVM) is a classification
and regression prediction tool that uses machine learning theory to maximize
predictive accuracy while automatically avoiding over-fit to the data. Support
Vector machines can be defined as systems which use hypothesis space of a
linear functions in a high dimensional feature space, trained with a learning
algorithm from optimization theory that implements a learning bias derived from
statistical learning theory. Su
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