Classification with application to Functional Data based on Gaussian process

Abstract

In \u00a0this \u00a0paper, \u00a0we \u00a0briefly \u00a0introduce \u00a0four \u00a0methods \u00a0for \u00a0functional \u00a0classification. \u00a0To \u00a0compare \u00a0the effects of the four \u00a0models, \u00a0we \u00a0generate the data \u00a0from \u00a0Gaussian process based on a functional \u00a0mixed-effects model, square exponential kernel is used in random-effect term to describe the nonlinear structure of the data. The outcomes show that the two functional classification models have a better prediction correct rate than the two machine learning classification models.