介绍RKHS中的（平滑样条）ANOVA模型，包括地理数据、医学、大气科学和机器学习中的示例
An introduction to (smoothing spline) ANOVA models in RKHS with examples in geographical data, medicine, atmospheric science and machine learning
摘要：
再生核希尔伯特空间（RKHS）中的平滑样条方差分析（SS-ANOVA）模型为各种领域中的数据分析、建模和学习提供了非常通用的框架。离散的、有噪声的、分散的、直接的和间接的观测可以被多个输入和多个可能相关的输出以及各种有意义的结构所容纳。本文的目的是简要概述的方法和描述和对比一系列的应用程序，同时注意到一些最近的结果。
英文摘要：
Smoothing Spline ANOVA (SS-ANOVA) models in reproducing kernel Hilbert spaces (RKHS) provide a very general framework for data analysis, modeling and learning in a variety of fields. Discrete, noisy scattered, direct and indirect observations can be accommodated with multiple inputs and multiple possibly correlated outputs and a variety of meaningful structures. The purpose of this paper is to give a brief overview of the approach and describe and contrast a series of applications, while noting some recent results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
