Package
weka.classifiers.functions
Synopsis
SVMreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm is selected by setting the RegOptimizer. The most popular algorithm (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default RegOptimizer.
For more information see:
S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999.
A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.
Options
The table below describes the options available for SVMreg.
Option | Description |
|---|---|
c | The complexity parameter C. |
debug | If set to true, classifier may output additional info to the console. |
filterType | Determines how/if the data will be transformed. |
kernel | The kernel to use. |
regOptimizer | The learning algorithm. |
Capabilities
The table below describes the capabilites of SVMreg.
Capability | Supported |
|---|---|
Class | Date class, Numeric class, Missing class values |
Attributes | Unary attributes, Nominal attributes, Numeric attributes, Missing values, Empty nominal attributes, Binary attributes |
Min # of instances | 1 |