Support Vector Machines for Classification Problems.- Method of Maximum Margin.-Dual Problem.-Soft Margin.- C- Support Vector Classification.-C- Support Vector Classification with Nominal Attributes.-LOO Bounds for Support Vector Machines.-Introduction.-LOO bounds for ε-Support Vector Regression.- LOO Bounds for Support Vector Ordinal Regression Machine.- Support Vector Machines for Multi-Class Classification Problems.-K-class Linear Programming Support Vector Classification Regression Machine (KLPSVCR).-Support Vector Ordinal Regression Machine for Multi-class Problems.- Unsupervised and Semi-Supervised Support Vector Machines.-Unsupervised and Semi-Supervised ν-Support Vector Machine.-Numerical Experiments.-Unsupervised and Semi-supervised Lagrange Support Vector Machine.-Unconstrained Transductive Support Vector Machine.-Robust Support Vector Machines.-Support Vector Ordinal Regression Machine.- Robust Multi-class Algorithm.- Robust Unsupervised and Semi-Supervised Bounded C-Support Vector Machine.-Feature Selection via lp-norm Support Vector Machines.-lp-norm Support Vector Classification.-lp-norm Proximal Support Vector Machine.-Multiple Criteria Linear Programming.-Comparison of Support Vector Machine and Multiple Criteria Programming.-Multiple Criteria Linear Programming.-Multiple Criteria Linear Programming for Multiple Classes.- Penalized Multiple Criteria Linear Programming.-Regularized Multiple Criteria Linear Programs for Classification.-MCLP Extensions.-Fuzzy MCLP.-FMCLP with Soft Constraints.-FMCLP by Tolerances.-Kernel Based MCLP.- Knowledge Based MCLP.- Rough set based MCLP.- Regression by MCLP.-Multiple Criteria Quadratic Programming.-A General Multiple Mathematical Programming.-Multi-Criteria Convex Quadratic Programming Model Kernel based MCQP.- Non-additiveMCLP.-Non-additive Measures and Integrals.-Non-additive Classification Models.-Non-additive MCP.- Reducing the Time Complexity.-Hierarchical Choquet Integral.-Choquet Integral with Respect to