  Most of the recent results in polynomial functional regression have been
focused on an in-depth exploration of single-parameter regularization schemes.
In contrast, in this study we go beyond that framework by introducing an
algorithm for multiple parameter regularization and presenting a theoretically
grounded method for dealing with the associated parameters. This method
facilitates the aggregation of models with varying regularization parameters.
The efficacy of the proposed approach is assessed through evaluations on both
synthetic and some real-world medical data, revealing promising results.
