Weka 3.7 Classifier

There are 120 nodes that can be used as predessesor for a node with an input port of type

RBFRegressor (3.7)

Class implementing radial basis function networks for classification, trained in a fully supervised manner using WEKA's Optimization class by minimizing […]

SGD (3.7)

Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and […]

SGDText (3.7)

Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data

SMO (3.7)

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

SMOreg (3.7)

SMOreg implements the support vector machine for regression

SPegasos (3.7)

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al

SimpleLinearRegression (3.7)

Learns a simple linear regression model

SimpleLogistic (3.7)

Classifier for building linear logistic regression models

VotedPerceptron (3.7)

Implementation of the voted perceptron algorithm by Freund and Schapire

Winnow (3.7)

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N

Node Connectivity