RBFNetwork

Package

weka.classifiers.functions

Synopsis

Class that implements a normalized Gaussian radial basisbasis function network.
It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class.It standardizes all numeric attributes to zero mean and unit variance.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (RBFNetwork).

Options

The table below describes the options available for RBFNetwork.

Option	Description
clusteringSeed	The random seed to pass on to K-means.
debug	If set to true, classifier may output additional info to the console.
maxIts	Maximum number of iterations for the logistic regression to perform. Only applied to discrete class problems.
minStdDev	Sets the minimum standard deviation for the clusters.
numClusters	The number of clusters for K-Means to generate.
ridge	Set the Ridge value for the logistic or linear regression.

Capabilities

The table below describes the capabilites of RBFNetwork.

Capability	Supported
Class	Date class, Numeric class, Binary class, Nominal class, Missing class values
Attributes	Missing values, Empty nominal attributes, Binary attributes, Numeric attributes, Nominal attributes, Unary attributes
Min # of instances	1