The use of Support Vector Machine (SVM) is similar to Discriminant Analysis and Logistic Regression Analysis. It also uses a line (super plane) to classify samples.
Support Vector Machine uses "margin." It is a function to evaluate the generality of the model.
It is used to avoid over-fitting and local optimization.
The formulation of SVM uses the all data. But the formulation of output includes only the data near the line. The data called "Support Vector".
The output of RapidMiner tells the information of support vectors.
There are cases that a simple line is not valid to classify. We can use simple line method by the help of Kernel method.
Kernel method is made in the studies of SVM. But this method also used for other multi-variable analysis methods.
Difference of Good Distribution by Methods
Difference of Output by Methods
Selection of Methods
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