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.
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 |