Node Connectivity
There are 99 nodes that can be used as predessesor
for a node with an input port of type 
Weka 3.6 Classifier.
MultilayerPerceptron (3.6) (legacy)
A Classifier that uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both. The network can also be […]
PLSClassifier (3.6) (legacy)
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
PaceRegression (3.6) (legacy)
Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the […]
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 […]
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 […]
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 […]
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 […]