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中文摘要：
基于最优化理论中的KKT互补条件建立支持向量回归机的无约束不可微优化模型,并给出了一种有效的光滑近似解法——调节熵函数方法.该方法不需参数取值很大便可逼近问题的最优解,从而避免了一般熵函数法为了逼近精确解,参数取得过大而导致数值的溢出现象,为求解支持向量回归机提供了一条新途径.数值实验结果表明,回归型支持向量机的调节熵函数法改善了支持向量机的回归性能和效率.
英文摘要：
Based on Karush-Kuhn-Tucker（KKT） complementary condition in optimization theory, unconstrained nondifferential optimization models for support vector regression are proposed, and an adjustable entropy function method is given. This method can find an optimal solution with a relatively small parameter. It avoids the numerical overflow in the entropy function methods available. It is a new approach to solve support vector regression. Numerical results show that the new approach improves the regression performance and increases the learning efficiency.
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