Examples of org.encog.ml.svm.SVM

org.encog.ml.svm.SVM

This is a network that is backed by one or more Support Vector Machines (SVM). It is designed to function very similarly to an Encog neural network, and is largely interchangeable with an Encog neural network. The support vector machine supports several types. Regression is used when you want the network to predict a value, given the input. Function approximation is a good example of regression. Classification is used when you want the SVM to group the input data into one or more classes. Support Vector Machines typically have a single output. Neural networks can have multiple output neurons. To get around this issue, this class will create multiple SVM's if there is more than one output specified. Because a SVM is trained quite differently from a neural network, none of the neural network training classes will work. This class must be trained using SVMTrain.

  }
  
  public void testFactorySVM() {
    String architecture = "?->C(KERNEL=RBF,TYPE=NEW)->?";
    MLMethodFactory factory = new MLMethodFactory();
    SVM network = (SVM)factory.create(MLMethodFactory.TYPE_SVM, architecture, 4, 1);
    Assert.assertEquals(4,network.getInputCount());
    Assert.assertEquals(1,network.getOutputCount());
  }

  @Override
  public MLMethod generate() {
    if (this.outputNeurons != 1) {
      throw new PatternError("A SVM may only have one output.");
    }
    final SVM network = new SVM(this.inputNeurons, this.svmType,
        this.kernelType);
    return network;
  }

Examples of org.encog.ml.svm.SVM

Related Classes of org.encog.ml.svm.SVM