Using an iterative linear solver in an interior-point method for generating support vector machines
Using an iterative linear solver in an interior-point method for generating support vector machines
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DOI：
10.1007/s10589-008-9228-z
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发表时间：
2010-11
影响因子：
2.2
通讯作者：
E. M. Gertz;Joshua D. Griffin
中科院分区：
文献类型：
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作者：
E. M. Gertz;Joshua D. Griffin
为了解决机器学习中的模式识别问题，研究了支持向量机分类器的生成问题。提出了一种基于内点方法的凸二次规划方法。这种内点法使用线性预条件共轭梯度法和一种新的预条件算子来计算每次迭代。通过将面向对象的程序包OOQP与问题结构相适应，开发了一个实现。给出了数值结果，并讨论了计算经验。
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.