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. …

This classifier generates a two-class kernel logistic regression model The model is fit by minimizing the negative log-likelihood…

A wrapper class for the liblinear classifier. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008) LIBL…

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 weighted…

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 pr…

Class implementing radial basis function networks for classification, trained in a fully supervised manner using WEKA's Optimizat…

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

Class implementing radial basis function networks for classification, trained in a fully supervised manner using WEKA's Optimizat…

Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, sq…

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

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

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

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 algorithm …

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al (2…

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