| Model Browser User's Guide | |
Lambda Selection Algorithms
Lambda is the regularization parameter.
IterateRidge
For a specified width, this algorithm optimizes the regularization parameter with respect to the GCV criterion (generalized cross-validation; see the discussion under GCV criterion).
The initial centers are selected either by one of the low-level center selection algorithms or the previous choice of centers is used (see discussion under the parameter Do not reselect centers). You can select an initial start value for 
by testing an initial number of values for lambda (set by the user) that are equally spaced on a logarithmic scale between 10-10 and 10 and choosing the one with the best GCV score. This helps avoid falling into local minima on the GCV - 
curve. The parameter 
is then iterated to try to minimize GCV using the formulas given in the GCV criterion section. The iteration stops when either the maximum number of updates is reached or the log10(GCV) value changes by less than the tolerance.
Fit Parameters
Center selection algorithm: The center selection algorithm to use.
Maximum number of updates: Maximum number of times that the update of 
is made. The default is 10.
Minimum change in log10(GCV): Tolerance. This defines the stopping criterion for iterating 
; the update stops when the difference in the log10(GCV) value is less than the tolerance. The default is 0.005.
Number of initial test values for lambda: Number of test values of 
to determine a starting value for 
. Setting this parameter to 0 means that the best 
so far is used.
Do not reselect centers for new width: This check box determines whether the centers are reselected for the new width value, and after each lambda update, or if the best centers to date are to be used. It is cheaper to keep the best centers found so far, and often this is sufficient, but it can cause premature convergence to a particular set of centers.
Display: When you select this check box, this algorithm plots the results of the algorithm. The starting point for 
is marked with a black circle. As 
is updated, the new values are plotted as red crosses connected with red lines. The best 
found is marked with a green asterisk.
A lower bound of 10-12 is placed on 
, and an upper bound of 10.
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