摘要
本文将支持向量机的算法引入到尿沉渣有形成分的分类问题上。在提取特征的基础上,采用交叉验证法和精度等高线图进行核函数及参数的选择,根据支持向量机和数据集特点,设计出由两级分类器集成的支持向量机多分类器,得到了相应的混淆矩阵。临床实验数据分类评测以及与神经网络方法比较结果表明,提出的算法具有一定的优势。
This article used sup[port vector machine(SVM) algorithm to recognize the particles in urine sediment in this paper. After feature extraction,cross-validation method and the contour chart of the accuracy were implemented to select the kernel function and the parameters of SVM,and according to the characteristics of SVM classifier and sample data, Multi-SVMs with two-level-classifier was successfully designed and A classification matrix was eventually obtained. The evaluation by using clinical data and compa...
出处
《中国医疗器械杂志》
CAS
2008年第6期409-412,共4页
Chinese Journal of Medical Instrumentation
关键词
统计学习理论
支持向量机
尿沉渣图像
交叉验证
混淆矩阵
statistical learning theory
support vector machine SVM
urine sediment
cross-validation
classification-matrix
