Using an iterative linear solver in an interior-point method for generating support vector machines

作者：Gertz E Michael^*; Griffin Joshua D

来源：Computational Optimization and Applications, 2010, 47(3): 431-453.

DOI：10.1007/s10589-008-9228-z

• Machine learning
• Support vector machines
• Quadratic programming
• Interior-point methods
• Krylov-space methods
• Matrix-free preconditioning

This paper concerns the generation of support vector machine classifiers for solving the pattern recognition problem in machine learning. A method is proposed based on interior-point methods for convex quadratic programming. This interior-point method uses a linear preconditioned conjugate gradient method with a novel preconditioner to compute each iteration from the previous. An implementation is developed by adapting the object-oriented package OOQP to the problem structure. Numerical results are provided, and computational experience is discussed.

• 出版日期2010-11