Robust and Interior Point Optimization Methods in Support Vector Machine Training

支持向量机训练中的鲁棒和内点优化方法

• 批准号: 9978813
• 负责人: Theodore Trafalis
• 金额: $ 16.22万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-15 至 2001-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9978813&HistoricalAwards=false
• 关键词: Robust; Interior; Point; Optimization; Methods

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.

9978813Trafalis本项目将研究基于内点方法（IPM）的支持向量机训练新算法。训练支持向量机（SVM）需要解决一个非常大的二次规划（QP）优化问题。PI将通过利用问题的特殊结构将这个大QP问题分解为一系列最小的可能QP子问题。IPM将用于解决由此产生的QP子问题。其目标是处理非常大的训练集，以分析海量数据。 有几类IPM可供选择来解决QP子问题。 PI将使用潜在的减少和列生成方法。SVM QP问题的特殊结构的研究将通过使用数学规划的分解技术进行检查。拟议的项目是多学科的，将数学规划和机器学习的最新技术结合起来，以解决大规模数据挖掘问题，并将其应用于气象学，地震学，高能物理学，天文学，金融和信用卡诈骗。它为训练大规模支持向量机提供了一种新的方法，对科学和工程基础设施将产生重大影响。