Support vector machines for classication and regression
Yizeng Liang, Qing-Song Xu, Hong-Dong Li, Dong-Sheng Cao · 2011
Appendix A: Computation of Slack Variable-Based SVMs ....................... 43 Appendix B: Computation of Linear ε-SVR ................................................. 44 References .......................................................................................................... 45 and regression based on whether the value of the output vector is discrete or continuous. In this sense, SVMs can be divided into two categories: support vector classication (SVC) machines and support vector regression (SVR) machines. According to this classication, the basic elements and algorithms of SVC and SVR are rst discussed both theoretically and experimentally in detail, respectively. Then two simulated datasets are employed to investigate the predictive performance of SVM. It is illustrated that SVMs can deal well with data of some nonlinearity. The applications of SVM to real-world data can be found in Chapters 5 through 8, respectively.