Support Vector Machines (SVM)

Support Vector Machines (SVMs) are powerful tools for data classification. SVM finds an optimal hyperplane that best segregates observations from different classes. Support vector machines (SVMs) are used for classification of both linear and nonlinear data. Classification is achieved by a linear or nonlinear mapping to transform the original training data into a higher dimension. Within this new dimension, it searches for the linear optimal separating hyperplane (i.e. a “decision boundary” separating the tuples of one class from another)....

March 11, 2014 · 3 min · 443 words · Pradeep Loganathan