支持向量回归的颅内压时间系列无损估计方法

Support Vector Regression Based Time Series Mining Approach for Non-Invasive ICP Assessment

  • 摘要: 在对时间序列数据挖掘框架进行研究时发现:在利用线性映射函数刻画误差和特征间的关系时,不能获得对颅内压力信号的精确估计。为了提高对颅内压估计的精确性,本文采用支持向量回归构建存在于特征和误差间的非线性映射函数,实验结果表明:基于支持向量回归的非线性映射函数预测效果明显优于先前所采用的线性最小二乘法所构成的线性映射函数策略。

     

    Abstract: For the data mining based on time series estimation, the existed studies reveal that the Intra-Cranial Pressure (ICP) time series cannot be well estimated when the linear mapping function is used to delineate the relationship between error and feature. To improve the accuracy for ICP estimation, the non-linear support vector regression (SVR) is used to construct the nonlinear function between feature and error. The experiment results showed that the SVR based mapping function is superior to the linear least square based one.

     

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