Examples of weka.classifiers.functions.supportVector.RBFKernel

Package weka.classifiers.functions.supportVector

Examples of weka.classifiers.functions.supportVector.RBFKernel

weka.classifiers.functions.supportVector.RBFKernel
The RBF kernel. K(x, y) = e^-(gamma * <x-y, x-y>^2)
Valid options are:
```
 -D Enables debugging output (if available) to be printed. (default: off)
```
```
 -no-checks Turns off all checks - use with caution! (default: checks on)
```
```
 -C <num> The size of the cache (a prime number), 0 for full cache and  -1 to turn it off. (default: 250007)
```
```
 -G <num> The Gamma parameter. (default: 0.01)
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @author Shane Legg (shane@intelligenesis.net) (sparse vector code) @author Stuart Inglis (stuart@reeltwo.com) (sparse vector code) @author J. Lindgren (jtlindgr{at}cs.helsinki.fi) (RBF kernel) @version $Revision: 5518 $

      // new svm with rbf kernel
      SMO smo = new SMO();
      String[] options = { "-L 1e-15", "-P 1e-15" , "-N 2"};
      smo.setOptions(options);
      smo.setC(1.0); //same as default value for jkms
      RBFKernel rbf = new RBFKernel();
      rbf.setGamma(0.1); //same as default value for jkms
      smo.setKernel(rbf);
      
      smo.buildClassifier(train);

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   * the default constructor
   */
  public GaussianProcesses() {
    super();
    
    m_kernel = new RBFKernel();
    ((RBFKernel) m_kernel).setGamma(1.0);
  }

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    @Override
    public WekaModel train(ExampleSet examples) {
        //These settings aren't terrible
        SMO smo = new SMO();
        RBFKernel rbf = new RBFKernel();
        rbf.setGamma(0.5);
        smo.setKernel(rbf);
        smo.setC(1.5);
        
        //These also work pretty ok
        Logistic log = new Logistic();

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