Robust and Interior Point Optimization Methods in Support Vector Machine Training

这个项目将研究基于内点方法(IPMS)训练支持向量机的新算法。训练支持向量机需要解决一个非常大的二次规划(QP)优化问题。PI通过利用问题的特殊结构，将这个大型的QP问题分解成一系列最小的可能的QP子问题。IPMS将用于解决由此产生的QP子问题。目标是处理用于分析海量数据的非常大的训练集。有几类IPM可供选择来解决QP子问题。PI将使用势归约和列生成方法。将使用数学规划的分解技术来研究支持向量机QP问题的特殊结构。建议的项目是多学科的，集成了数学规划和机器学习的最新技术来解决大规模数据挖掘问题，应用于气象学、地震学、高能物理、天文学、金融和信用卡诈骗。它为训练大规模的支持向量机提供了一种新的途径，将对科学和工程的基础设施产生重大影响。

9978813TrafalisThis project will investigate new algorithms for training Support Vector Machines based on Interior Point Methods (IPMs). Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. The PI will break this large QP problem into a series of smallest possible QP subproblems by exploiting the special structure of the problem. IPMs will be used for the solution of the resulting QP subproblems. The objective is to handle very large training sets for analyzing massive data. There several classes of IPMs from which to choose to solve the QPsubproblems. The PI will use potential reduction and column generation methods.Investigation of the special structure of the SVM QP problem will be examined by using decomposition techniques of mathematical programming.The proposed project is multidisciplinary, integrating state of the art techniques of mathematical programming and machine learning to solve large scale data mining problems with applications to meteorology, seismology, high energy physics, astronomy, finance and credit card fraud. It will have a significant effect on infrastructure of science and engineering since it presents a new approach for training large scale SVMs.