Support Vector Regression Based on Goal Programming and Multi-objective Programming
Hirotaka Nakayama, Yeboon Yun · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Support Vector Mechine (SVM) is gaining much popularity as a powerful machine learning technique. SVM was originally developed for pattern classification and later extended to regression. One of main feature of SVM is that it generalizes the maximal margin linear classifiers into high dimensional feature spaces through nonlinear mappings defined implicitly by kernels in the Hilbert space so that it may produce nonlinear classifiers in the original data space. On the other hand, the authors developed a family of various SVMs using multi-objective programming and goal programming (MOP/GP) techniques. This paper extends the family of SVM for classification to regression, and discusses their performance through numerical experiments.