In recent years, there has been considerable innovation in the world of predictive methodologies. This is evident by the relative domination of machine learning approaches in various classification competitions. While these algorithms have excelled at multivariate problems, they have remained dormant in the realm of functional data analysis. We extend notable deep learning methodologies to the domain of functional data for the purpose of classification problems. We highlight the effectiveness of our method in a number of classification applications such as classification of spectrographic data. Moreover, we demonstrate the performance of our classifier through simulation studies in which we compare our approach to the functional linear model and other conventional classification methods.

翻译：近年来,在预测方法方面,世界出现了相当大的创新,在各种分类竞争中,机器学习方法相对占优势,这一点显而易见。虽然这些算法在多变问题方面优于多变问题,但在功能数据分析领域却一直处于停滞状态。我们为了分类问题的目的,将显著的深层次学习方法推广到功能数据领域。我们强调我们的方法在一些分类应用中的有效性,例如光谱数据的分类。此外,我们通过模拟研究展示了我们分类方法的绩效,我们在模拟研究中比较了我们的方法与功能线性模型和其他常规分类方法。