论文标题
高维时间序列的纵向支持向量机
Longitudinal Support Vector Machines for High Dimensional Time Series
论文作者
论文摘要
我们考虑从观察到的功能数据中学习分类器的问题。在这里，每个数据点采用单个时间序列的形式，并包含许多功能。假设每个这样的系列都带有二进制标签，则考虑了学习预测新的即将到来的时间序列标签的问题。迄今为止，{\ em Margin}的概念将经典支持向量机的基础扩展到连续版本的此类数据。纵向支持向量机也是凸优化问题，其双重形式也被得出。具有显着性检验的特定病例的经验结果表明，该创新算法在分析这种长期多元数据的功效。
We consider the problem of learning a classifier from observed functional data. Here, each data-point takes the form of a single time-series and contains numerous features. Assuming that each such series comes with a binary label, the problem of learning to predict the label of a new coming time-series is considered. Hereto, the notion of {\em margin} underlying the classical support vector machine is extended to the continuous version for such data. The longitudinal support vector machine is also a convex optimization problem and its dual form is derived as well. Empirical results for specified cases with significance tests indicate the efficacy of this innovative algorithm for analyzing such long-term multivariate data.
