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functions

This category contains 18 nodes.

RBFNetwork (3.6)

Class that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and […]

SimpleLinearRegression (3.6)

Learns a simple linear regression model. Picks the attribute that results in the lowest squared error. Missing values are not allowed. Can only deal with […]

SimpleLogistic (3.6)

Classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used for fitting the logistic […]

SMO (3.6)

Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all […]

SMOreg (3.6)

SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm is selected by setting […]

SPegasos (3.6)

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation […]

VotedPerceptron (3.6)

Implementation of the voted perceptron algorithm by Freund and Schapire. Globally replaces all missing values, and transforms nominal attributes into binary […]

Winnow (3.6)

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant […]