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.