Optimized fixed-size kernel models for large data sets

A modified active subset selection method based on quadratic Rényi entropy and a fast cross-validation for fixed-size least-squares support vector machines is proposed for classification and regression with optimized tuning process. The kernel bandwidth of the entropy based selection criterion is optimally determined according to the solve-the-equation plug-in method. Also a fast cross-validation method based on a simple updating scheme is developed. The combination of these two techniques is suitable for handling large-scale data sets on standard personal computers. Finally, the performance on test data and computational time of this fixed-size method is compared to standard support vector machines and ν-support vector machines resulting in sparser models with lower computational cost and comparable accuracy.