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中文摘要：
广义特征值中心支持向量回归机（GEPSVR）是一种有效的核回归算法，但其在求解优化问题时易导致奇异性问题．为此，提出一种基于特征值分解的支持向量回归机，简称IGEPSVR．与GEPSVR相比，IGEPSVR的主要优势有：结合最大间隔准则和GEPSVR几何思想给出了新的距离度量准则；在优化模型中引入Tikhonov正则项，克服了可能产生的奇异性问题：IGEPSVR仅需求解两个标准特征值，降低了计算复杂度．实验结果表明，较GEPSVR算法，IGEPSVR不仅提高了学习能力，而且缩短了训练时间．
英文摘要：
The generalized eigenvalue proximal support vector regressor（GEPSVR） is an effective kernel-based regression algorithm. However, the generalized eigenvalue problems may be ill-conditioned in the GEPSVR. Therefore, a maximum margin eigenvalue proximal support vector regressor（IGEPSVR） is proposed. The main advantages are as following by defining the distances between the insensitive functions and data points, a novel optimization model is proposed according to the maximum margin criterion and GEPSVR; the possible ill-conditioned problem is overcome by introducing the meaningful Tikhonov regularization terms; the generalized eigenvalue decomposition is replaced by the standard eigenvalue decomposition, leading to simpler optimization problems. Experimental results on a series of datasets show that IGEPSVR is superior to GEPSVR in both generalization and training speed.
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