Support vector machines are used in data mining (predictive modeling, to be specific) for classification of records, by learning from training data. Support vector machines use decision surfaces that separate records. They rely on optimization techniques to maximize separate margins between classes, and kernel functions to accommodate non-linearity. Model parameters can be tuned on the basis of model performance with hold-out samples.
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