Model Browser User's Guide

Width Selection Algorithm: TrialWidths

This is the same algorithm as is used in ordinary RBFs, that is, a guided search for the best width parameter.

Lambda and Term Selection Algorithms: Interlace

This algorithm is a generalization of StepItRols for RBFs. The algorithm chooses radial basis functions and linear model terms in an interlaced way, rather than in two steps. At each step a forward search procedure is performed to select the radial basis function (with a center chosen from within the set of data points) or the linear model term (chosen from the ones specified in the linear model setup pane) that decreases the regularized error the most. This process continues until the maximum number of terms is chosen. The first few terms are added using the stored value of lambda. After StartLamUpdate terms have been added, lambda is iterated after each center is added to improve GCV.

The fit options for this algorithm are as follows:

• Maximum number of terms: Maximum number of terms that will be chosen. The default is a quarter of the data points, or 25, whichever is smaller.
• Maximum number of centers: Maximum number of terms that can be radial basis functions. The default is the same as the maximum number of terms.
• Percentage of data to be candidate centers: Percentage of the data points that are available to be chosen as centers. The default is 100% when the number of data points is <=200.
• Number of terms to add before updating: How many terms to add before updating lambda begins.
• Minimum change in log10(GCV): Tolerance.
• Maximum no. times log10(GCV) change is minimal: Number of steps in a row that the change in log10(GCV) can be less than the tolerance before the algorithm terminates.

Lambda and Term Selection Algorithms: TwoStep

This algorithm starts by fitting the linear model specified in the linear model pane, and then fits a radial basis function network to the residual. You can specify the linear model terms to include in the usual way using the term selector. If desired, you can activate the stepwise options. In this case, after the linear model part is fitted, some of the terms are automatically added or removed before the RBF part is fitted. You can choose the algorithm and options that are used to fit the nonlinear parameters of the RBF by pressing the Set Up button in the RBF training options.

Hybrid Radial Basis Functions

Tips for Modeling with Radial Basis Functions