3.1 Support Vector Classifier . . . . . . . 37
3.2 SVC with Soft Margin and Optimization . .. . 41
3.3 Kernel Trick . . . . . . .. . . . . . . . . . 42
3.4 Theoretical Foundations . . . . . . . . 47
3.5 Support Vector Regressor . . . . . . .. . . . . . 50
3.6 Software Implementations . . . . . . . . . . . 52
3.7 Current and Future Research . . . . . . . . . 52
3.7.1 Computational Efficiency . . . . . . . . . 52
3.7.2 Kernel Selection . . . . . . .. . . . 53
3.7.3 Generalization Analysis . . . . . . . 53
3.7.4 Structural SVM Learning . . . . . . . . 54
3.8 Exercises . . . . . . . . . 55
References . . . . .. . . 56