Support Vector machines is a machine learning technique for classifying high-dimensional data into two classes. It creates a hyperplane, with the largest amount of separation between the two classes. The classifier is made linear, by transforming the original features of input data into new features. SVMs use kernel methods to learn from specific instance, that are close to the decision boundary. SVMs are used for text mining, such as spam filtering and outlier detection.
