论文标题
神经网络作为功能分类器
Neural Networks as Functional Classifiers
论文作者
论文摘要
近年来，在预测方法的世界中，已经有了可观的创新。在各种分类竞赛中，机器学习方法的相对支配可以明显看出这一点。尽管这些算法在多元问题方面表现出色，但在功能数据分析领域仍然处于休眠状态。我们将显着的深度学习方法扩展到功能数据领域，目的是出于分类问题的目的。我们强调了我们方法在许多分类应用中的有效性，例如光谱数据的分类。此外，我们通过仿真研究证明了分类器的性能，在这些研究中，我们将我们的方法与功能线性模型和其他常规分类方法进行了比较。
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.
