Mathematical Programming in Data Mining

数据挖掘中的数学规划

• 批准号: 0138308
• 负责人: Olvi Mangasarian
• 金额: $ 25.48万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-06-15 至 2008-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0138308&HistoricalAwards=false
• 关键词: Mathematical; Programming; Data; Mining

ABSTRACT0138308Olvi MangssarianU of Wisconsin-MadisonSupport vector machines (SVMs) have played a key role in broad classes of problems arising in various fields. Much more recently, SVMs have become the tool of choice for problems arising in data mining. This proposal de-scribes some aspects of support vector machines that the proposer would like to study and contribute further to. These include: generalized SVMs (a gen-eral mathematical programming framework for SVMs), unconstrained SVMs (a strongly convex minimization reformulation of SVMs solvable by a fastfinite method), Lagrangian classification (an unconstrained Lagrangian rep-resentation of SVMs leading to an extremely simple iterative scheme capable of solving classification problems with millions of data points) and reduced data classification (a rectangular kernel classifier that is characterized by as little as 1% of the data).

威斯康星-麦迪逊支持向量机(SVM)的Olvi MangssarianU在不同领域出现的各种问题中发挥了关键作用。最近，支持向量机已经成为解决数据挖掘中出现的问题的首选工具。这项建议描述了支持向量机的一些方面，建议者希望对其进行研究并做出进一步贡献。它们包括：广义支持向量机(一种通用的支持向量机数学规划框架)、无约束支持向量机(一种可用快速有限方法求解的支持向量机的强凸极小化改写)、拉格朗日分类(一种无约束的支持向量机的拉格朗日表示法，导致能够解决具有数百万个数据点的分类问题的极其简单的迭代方案)和简化数据分类(一种矩形核分类器，其特征在于只有1%的数据)。