Regression estimation with support vector learning machines
1 Support Vector Learning Machines (SVLM) have become an emerging technique which has proven successful in many traditionally neural network dominated applications. This is also the case for Regression Estimation (RE). In particular we are able to construct spline approximations of given data independently from the number of input-dimensions regarding complexity during training and with only linear complexity for reconstruction-compared to exponential complexity in conventional methods.