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Method for Classify.
Models class probabilities by finding a hyperplane that separates the training data into two classes using a maximum-margin hyperplane.

Examplesopen all close all 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:

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