Models class probabilities by finding a hyperplane that separates the training data into two classes using a maximum-margin hyperplane.
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:
uses an exponential radial basis function as kernel
the strategy to use to obtain a multiclass classifier
the degree of the polynomial d in the polynomial kernel
Possible settings for "MulticlassStrategy" include:
train a binary classifier for each pair of classes
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.
open allclose all
Train a classifier function on labeled examples:
Obtain information about the classifier:
Classify a new example:
Generate some data that is not linearly separable:
Train a classifier on this dataset:
Plot the training set and the probability distribution of each class as a function of the features: