Comparison of Second-Order Polynomial Model Selection Methods: An Experimental Survey

Grace W. Rumantir, Monash University

This abstract gives an overview of the work described in (Rumantir 1999). The paper compares some of the most commonly cited model selection criteria that claim to have the mechanism to balance between model complexity and goodness of t. The model chosen by any of the methods is claimed to be a parsimonious description of the data at hand, therefore has predictive power for future data.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.