functions
This category contains 18 nodes.
RBFNetwork (3.6) (legacy)
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) (legacy)
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) (legacy)
Classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used for fitting the logistic […]
SMO (3.6) (legacy)
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all […]
SMOreg (3.6) (legacy)
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) (legacy)
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) (legacy)
Implementation of the voted perceptron algorithm by Freund and Schapire. Globally replaces all missing values, and transforms nominal attributes into binary […]
Winnow (3.6) (legacy)
Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant […]