Applying the SVC model algorithm to solving the SVR model often needs the SVR kernel function is positive and satisfies the Mercer conditions, but in actual applications, while using the SVR geometric frame to convert the SVC kernel function to the corresponding SVR kernel function, we usually cannot guarantee the converting kernel function is qualitative, which leads to that the SVR optimization model is not a convex programming model that we cannot solve it. To solve these problems, we propose to use the extended SMO algorithm to solve the SVR problem with a non-positive kernel and de-velope its working set selection rule, analyze how to select in the algorithm the current work collection's optimal variable value. Because the algorithm does not require the kernel function is positive, it expands the range of the kernel function's options. The simulation results show that this algorithm for the non-positive kernel function SVR model has good generalization performance and regression accuracy, featuring a certain theoretical and practical significance.%将求解SVC模型的算法运用到求解SVR模型中一般要SVR模型的核函数正定且满足Mercer条件,而实际应用中利用几何框架将SVC模型转换成相应的SVR模型时,通常无法保证经转换得到的SVR模型的核函数具有正定性,从而导致SVR模型不是凸规划模型而无法求解.为解决上述问题,本文提出了一种运用扩展的序列最小最优化方法(SMO)来求解基于非正定核的SVR模型,设计了算法中工作集的选择准则,解决了算法中如何选择工作集变量当前的最优值问题.由于该算法不要求核函数具有正定性,从而拓宽了SVR模型核函数的选择范围.实验表明,该算法对基于正定或非正定核的SVR模型都具有很好的泛化性能和回归精度,具有一定的理论意义和实用价值.
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