SBIR Phase I: Automating Regression - An Optimization Approach

SBIR 第一阶段：自动回归 - 一种优化方法

• 批准号: 0128103
• 负责人: Georgia Mourtzinou
• 金额: $ 10万
• 依托单位: DYNAMIC IDEAS, LLC
• 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: 2002-01-01 至 2002-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0128103&HistoricalAwards=false
• 关键词: SBIR; Phase; Automating; Regression; Optimization

The Small Business Innovation Phase I Research will develop a formal algorithm, and a corresponding software package, which constructs and evaluates regression models automatically and can handle large data sets efficiently. Regression analysis is widely used in all areas of physical and social sciences, medicine, management, and engineering. Despite its widespread use, there is a significant element of art in building regression models. In addition, the wide availability of data makes it important to be able to perform regressions with very large data sets and a large number of potential explanatory variables. The objective is to take the art out of the construction of regression models, and, in doing so, will achieve better regression models that are significantly faster, and extend their capabilities to build much larger models in the presence of very large data sets.The commercial benefits of an automated regression package would allow data mining in diverse areas such as marketing, health care, insurance, credit cards, and finance. An automated regression package that can handle very large data sets and many explanatory variables can become the critical engine for data mining in all these industries.

小企业创新第一阶段研究将开发一种正式的算法和相应的软件包，自动构建和评估回归模型，并能有效地处理大型数据集。回归分析广泛应用于物理和社会科学、医学、管理和工程的所有领域。尽管它被广泛使用，但在构建回归模型时有一个重要的艺术元素。此外，数据的广泛可用性使得能够对非常大的数据集和大量潜在的解释变量进行回归变得非常重要。其目标是将艺术从回归模型的构建中解放出来，并且，这样做，将实现更好的回归模型，这些模型的速度明显更快，并且扩展它们的能力，以在非常大的数据集上构建更大的模型。自动回归包的商业利益将允许在不同领域进行数据挖掘，例如营销，医疗保健，保险，信用卡和金融。 一个可以处理非常大的数据集和许多解释变量的自动回归包可以成为所有这些行业中数据挖掘的关键引擎。