摘要翻译：
在许多应用中，输入数据是在无限维空间中取其值的采样函数，而不是标准向量。这一事实对数据分析算法产生了复杂的后果，从而促使它们进行修改。事实上，大多数传统的回归、分类和聚类数据分析工具都以函数数据分析(FDA)的总称适应于函数输入。本文研究了支持向量机在函数数据分析中的应用，重点研究了曲线判别问题。支持向量机是一种利用核函数将所考虑的数据隐式非线性映射到高维空间的大边界分类器工具。我们展示了如何定义简单的内核，这些内核考虑到数据的功能性质，并导致一致的分类。在真实世界数据上进行的实验强调了考虑问题的某些功能方面的好处。
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英文标题：
《Support vector machine for functional data classification》
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作者：
Fabrice Rossi (INRIA Rocquencourt / INRIA Sophia Antipolis), Nathalie
  Villa (GRIMM)
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  In many applications, input data are sampled functions taking their values in infinite dimensional spaces rather than standard vectors. This fact has complex consequences on data analysis algorithms that motivate modifications of them. In fact most of the traditional data analysis tools for regression, classification and clustering have been adapted to functional inputs under the general name of functional Data Analysis (FDA). In this paper, we investigate the use of Support Vector Machines (SVMs) for functional data analysis and we focus on the problem of curves discrimination. SVMs are large margin classifier tools based on implicit non linear mappings of the considered data into high dimensional spaces thanks to kernels. We show how to define simple kernels that take into account the unctional nature of the data and lead to consistent classification. Experiments conducted on real world data emphasize the benefit of taking into account some functional aspects of the problems.
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PDF链接：
https://arxiv.org/pdf/705.0209