Does Weka's SimpleLogistic function use regularization?

I am wondering if Weka's Simple Logistic Function uses regularization.

I do not see any mention of it in the documentation, but I am not sure. Can anybody confirm this?

*classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used for fitting the logistic models. The optimal number of LogitBoost iterations to perform is cross-validated, which leads to automatic attribute selection. For more information see:

Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005.

OPTIONS

debug -- If set to true, classifier may output additional info to the console. useCrossValidation -- Sets whether the number of LogitBoost iterations is to be cross-validated or the stopping criterion on the training set should be used. If not set (and no fixed number of iterations was given), the number of LogitBoost iterations is used that minimizes the error on the training set (misclassification error or error on probabilities depending on errorOnProbabilities).

errorOnProbabilities -- Use error on the probabilties as error measure when determining the best number of LogitBoost iterations. If set, the number of LogitBoost iterations is chosen that minimizes the root mean squared error (either on the training set or in the cross-validation, depending on useCrossValidation). weightTrimBeta -- Set the beta value used for weight trimming in LogitBoost. Only instances carrying (1 - beta)% of the weight from previous iteration are used in the next iteration. Set to 0 for no weight trimming. The default value is 0. numBoostingIterations -- Set fixed number of iterations for LogitBoost. If >= 0, this sets the number of LogitBoost iterations to perform. If < 0, the number is cross-validated or a stopping criterion on the training set is used (depending on the value of useCrossValidation).

useAIC -- The AIC is used to determine when to stop LogitBoost iterations (instead of cross-validation or training error).

heuristicStop -- If heuristicStop > 0, the heuristic for greedy stopping while cross-validating the number of LogitBoost iterations is enabled. This means LogitBoost is stopped if no new error minimum has been reached in the last heuristicStop iterations. It is recommended to use this heuristic, it gives a large speed-up especially on small datasets. The default value is 50.

maxBoostingIterations -- Sets the maximum number of iterations for LogitBoost. Default value is 500, for very small/large datasets a lower/higher value might be preferable.*