A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classif…

Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighte…

Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, howeve…

A Classifier that uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both.…

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is p…

Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation g…

SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorith…

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

Implementation of the voted perceptron algorithm by Freund and Schapire. Globally replaces all missing values, and transforms nom…

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickl…

Implements Gaussian Processes for regression without hyperparameter-tuning. For more information see David J.C. Mackay (1998). In…

Learns an isotonic regression model. Picks the attribute that results in the lowest squared error. Missing values are not allowed…

Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions. Least…

A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this c…

A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions. (based on WEKA 3.6) For further…

Class that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provi…

Learns a simple linear regression model. Picks the attribute that results in the lowest squared error. Missing values are not all…

Classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used f…

Implements Gaussian processes for regression without hyperparameter-tuning To make choosing an appropriate noise level easier, th…

Learns an isotonic regression model Picks the attribute that results in the lowest squared error.Missing values are not allowed. …