"SupportVectorMachine" (Machine Learning Method)

Details & Suboptions

  • Support vector machines are binary classifiers. A kernel function is used to extract features from the examples. At training time, the method finds the maximum-margin hyperplane that separates classes. The multiclass classification problem is reduced to a set of binary classification problems (using a one-vs.-one or a one-vs.-all strategy). The current implementation uses the LibSVM framework in the back end.
  • The option "KernelType" allows you to choose the type of kernel to use. Possible settings for "KernelType" include:
  • "RadialBasisFunction"uses an exponential radial basis function as kernel
    "Polynomial"uses a polynomial function as kernel
    "Sigmoid"uses a sigmoidal function as kernel
    "Linear"uses a linear function as kernel
  • The kernel "RadialBasisFunction" takes the form:
  • The kernel "Polynomial" takes the form:
  • The kernel "Sigmoid" takes the form:
  • The kernel "Linear" takes the form:
  • The following options can be given:
  • "BiasParameter"1bias term c in polynomial and sigmoid kernels
    "GammaScalingParameter" Automaticthe parameter in the preceding kernels
    "KernelType" "RadialBasisFunction"the kernel to use to map to higher dimensions
    "MulticlassStrategy" Automaticthe strategy to use to obtain a multiclass classifier
    "PolynomialDegree" 3the degree of the polynomial d in the polynomial kernel
  • Possible settings for "MulticlassStrategy" include:
  • "OneVersusOne"train a binary classifier for each pair of classes
    "OneVersusAll"train one binary classifier for each class
  • The "GammaScalingParameter" controls the influence of the support vectors. Large values of gamma mean small radius of influence.
  • The "PolynomialDegree" option is specific to the polynomial kernel type.
  • The "MulticlassStrategy" option is used to generalize binary classifiers to a multiclass ones. The "OneVersusOne" strategy tests each class again each other, while the "OneVersusAll" strategy only test each class against the rest of the training set.


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Basic Examples  (2)

Train a classifier function on labeled examples:

Obtain information about the classifier:

Classify a new example:

Generate some data that is not linearly separable:

Visualize it:

Train a classifier on this dataset:

Plot the training set and the probability distribution of each class as a function of the features:

Options  (5)

"GammaScalingParameter"  (1)

Train a classifier with a specific value for the "GammaScalingParameter" suboption:

The "GammaScalingParameter" controls the influence of the support vectors.

Generate some data and visualize it:

Train two classifiers by changing the "GammaScalingParameter":

Look at how they perform on a test set to see how the radius of influence has changed:

"KernelType"  (2)

Train a classifier using a specific "KernelType":

Train two classifiers using different instances of "KernelType":

Compare their performance:

"MulticlassStrategy"  (1)

Use the "FisherIris" dataset to train two classifiers with different "MulticlassStrategy" options:

Look at their accuracy on a test set. The "OneVersusOne" option typically performs better.

"PolynomialDegree"  (1)

Use the "Mushroom" training set to train two classifiers using different degrees for the polynomial kernel type:

Compare the corresponding training times: