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大部分研究成果集中于对单参数正则化方案的深入探索。相比之下，本研究突破了这一框架，引入了一种多参数正则化算法，并提出了一种处理相关参数的理论支撑方法。该方法有助于聚合具有不同正则化参数的模型。通过在合成数据及部分真实医学数据上的评估，验证了所提方法的有效性，结果表明该方法具有良好前景。