基于层次支持向量机的脉搏信号情感识别  

Emotion Recognition of Pulse Signal Based on Hierarchical Support Vector Machine

作　　者：杜昭慧 司玉娟[1,2] 

机构地区：[1]吉林大学通信工程学院,长春130012 [2]吉林大学珠海学院,广东珠海519041

出　　处：《吉林大学学报（信息科学版）》2017年第1期37-42,共6页Journal of Jilin University（Information Science Edition）

基　　金：吉林省重点科技攻关基金资助项目(20150204039GX);长春市重大科技攻关专项基金资助项目(14KG064);广东省自然科学基金资助项目(2016A030313658);吉林省科技发展计划基金资助项目(20170414017GH)

摘　　要：针对传统支持向量机的情感识别中,随着识别情感的类别增加,支持向量机数目急剧增加,导致训练难度增大的同时占用内存空间过大,耗时过长的问题,提出了基于层次支持向量机的情感识别算法。该算法结合了二维情感模型理论,以层次支持向量机为基础,运用了小波分解等技术手段,构建了一套完整的脉搏信号情感识别方法。对于n类分类问题,传统的SVM(Support Vector Machine)分类需要n(n-1)/2个分类器,运用层次SVM分类只需要构造n-1个SVM分类器。实验结果表明,层次支持向量机模型在保证分类准确率的同时,减少了传统分类算法支持向量机的个数,分类速度提升了43.5%。As the recognition of the emotional category increases in emotion recognition,the number of support vector machine based on SVM（Support Vector Machine） classification increases dramatically. It results in training difficulty,taking up too much memory space and needing too long time. To solve this problem,we propose an emotion recognition algorithm based on hierarchical support vector machine. The algorithm combines the two-dimensional model for emotion theory,based on hierarchical support vector machine,with the help of wavelet decomposition and other technical means to design a hierarchical structure that is more persuasive than the traditional sentiment classification. A complete set of emotion recognition method for pulse signal is constructed. The experimental results show that this method can reduce the number of support vector machine and improve the classification speed under the premise of computing under a high classification accuracy of 43. 5%.

关 键 词：情感识别 脉搏 层次结构 支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]