Learning with Multiple Gaussian Distance Kernels for Time Series Classification

摘要：

Various distance measures have been proposed for time series classification, and several of them have been used to construct Gaussian distance kernels for support vector machine (SVM) - based classification. Considering that different Gaussian distance kernels may carry complementary information for classification, in this paper, we propose a multiple kernel learning (MKL) method to integrate multiple Gaussian distance kernels to further improve time series classification accuracy. We first adopt the classical Gaussian RBF (GRBF) kernel and the recently developed Gaussian elastic metric distance kernel (i.e. GERP kernel and GTWED kernel), and then use an efficient MKL, SimpleMKL, to learn the kernel classifier. Our experimental results on 12 VCR time series data sets show that the proposed method is superior to SVM with individual Gaussian distance kernel.

关键词： time series classification multiple kernel learning kernel method support vector machine

作者: Lei Liu Wangmeng Zuo David Zhang Dongyu Zhang

作者单位: Biocomputing Research Centre, School of Computer Science and Technology Harbin Institute of Technology Harbin, 150001, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

会议地点: 哈尔滨

会议语种:英文

页码: 624-628

在线出版日期: 2011-01-18（万方平台首次上网日期，不代表论文的发表时间）