基于模糊的最小支持响亮机,用来分类效果比一些常用的要好很多.-based on fuzzy loud support of the smallest machines, used classification results than some frequently used a lot nicer.

对目标函数的松弛变量引入一个参数μ，优化基本的最小二乘一类支持向量机算法。同样是解决线性问题，避免了二次规划的复杂问题(A parameter min is introduced into the relaxation variable of the objective function, and the basic least squares support vector machines algorithm is optimized