The Training of Support Vector Machines

 

In Chapter , the formulation of SVM concluded that SVM can be trained by solving Eqn. , subject to the constraints in Eqn. . This is standard constrained Quadratic Programming. In this chapter, the practical problems in solving this QP problem will be discussed. The effects of using different solvers are shown in Section . Section and show two major approaches that make the training of large classification problems tractable. In Section , the complexity and scalability of SVM training are discussed.

• Solving the Quadratic Programming Problem
  • Comparison of training results from the three different solvers
  • Reduction of Execution Time of QP Solvers
• Decomposition Algorithm for the SVM training
• Incremental Training of the Support Vector Machine
• Complexity and Scalability of the Training Algorithm