Abstract: 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.

Key words: lake water environment, support vector machines, classification algorithms, water quality evaluation