Support Vector Machine Algorithms

The Support Vector Machines (SVM) algorithms are a suite of algorithms that can be used with variety of problems and data. By changing one kernel for another, SVM can solve a variety of data mining problems. Oracle Data Mining supports two kernel functions, Linear and Gaussian; see SVM Kernel Functions.

SVM can emulate traditional methods, such as linear regression and neural nets, but goes far beyond those methods in flexibility, scalability, and speed.

SVM can be used to solve the following kinds of problems: classification, regression, and anomaly detection.

Oracle Data Mining uses SVM as the one-class classifier for anomaly detection. When SVM is used for anomaly detection, it has the classification mining function but no target. Applying a One-class SVM model results in a prediction and a probability for each case in the scoring data. If the prediction is 1, the case is considered typical. If the prediction is 0, the case is considered anomalous.

For more information about SVM, see How Support Vector Machines Work and Oracle Data Mining Concepts.