摘要
支持向量机(SVM)是由Vapnik等人提出的建立在统计学习理论基础上的一种小样本机器学习方法,最初用于解决二分类问题。由于使用结构风险最小化原则代替经验风险最小化原则,使它较好地解决了小样本情况下的学习问题。又由于采用了核函数思想,使它将非线性问题转化为线性问题来解决,降低了算法的复杂度。利用支持向量机多类分类算法,构建湖泊水环境评价模型。实验结果表明,该方法能够正确地对湖泊水环境质量进行分类评价。
Support vector machines （SVM） were developed from the machine learning theory of small samples based on statistical learning theory （SLT） by Vapnik et al, which were originally designed for binary classification problems. It can solve small-sample learning problems better by using structural risk minimization in place of experiential risk minimization. Moreover, SVM can convert a nonlinear learning problem into a linear learning problem in order to reduce the algorithm complexity by using the kernel function concept. A multi-class classification method of SVM is applied to lake water quality assessment. A case study shows that the method is reliable in the classification and evaluation of lake water quality.
出处
《吉林大学学报（地球科学版）》
EI
CAS
CSCD
北大核心
2006年第4期570-573,共4页
Journal of Jilin University：Earth Science Edition
基金
国家"973"项目(G1999045705)
关键词
湖泊
支持向量机
分类算法
水质评价
lake water environment
support vector machines
classification algorithms
water quality evaluation
