SVM::train

(PECL svm >= 0.1.0)

SVM::train — Create a SVMModel based on training data

public svm::train(array $problem, array $weights = ?): SVMModel

Train a support vector machine based on the supplied training data.

problem: The problem can be provided in three different ways. An array, where the data should start with the class label (usually 1 or -1) then followed by a sparse data set of dimension => data pairs. A URL to a file containing a SVM Light formatted problem, with the each line being a new training example, the start of each line containing the class (1, -1) then a series of tab separated data values shows as key:value. A opened stream pointing to a data source formatted as in the file above.
weights: Weights are an optional set of weighting parameters for the different classes, to help account for unbalanced training sets. For example, if the classes were 1 and -1, and -1 had significantly more example than one, the weight for -1 could be 0.5. Weights should be in the range 0-1.

Returns an SVMModel that can be used to classify previously unseen data. Throws SVMException on error