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

• Gaussian

The key features of SVM are:

• 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, then the case is considered typical. If the prediction is 0, then the case is considered anomalous.