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.

翻译：近年来多项式函数回归领域的研究成果主要集中于对单参数正则化方案的深入探索。相比之下，本研究突破该框架，提出了一种多参数正则化算法，并给出了一种理论依据充分的相关参数处理方法。该方法能够有效整合具有不同正则化参数的模型。通过合成数据及若干真实医学数据的评估，所提方法展现出良好的性能，取得了具有前景的结果。