New Directions in Predictive Learning for Classification

分类预测学习的新方向

• 批准号: 9704431
• 负责人: Jerome Friedman
• 金额: $ 39.87万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-15 至 2003-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9704431&HistoricalAwards=false
• 关键词: New; Directions; Predictive; Learning; Classification

Friedman 9704431 A predictive learning system is a computer program that constructs rules for predicting values of some property of a real system ("output") given the values of other properties ("inputs") of that system. Predictive learning systems attempt to construct or "learn" useful prediction rules purely by processing data taken from past successfully solved cases; that is, cases for which the values of both the output and input variables have been determined. The role of the learning algorithm is to automatically extract and organize the information in this data to obtain accurate rules for predicting output values of new cases in which only the values of the input variables are known. This research concentrates on new paradigms for the development of predictive learning methodology quite different from past approaches. The goal is to produce faster more powerful learning algorithms. These new algorithms can then be applied to solve broader classes of more complex problems, such as those that arise in the fields of communications, biotechnology, environmental forecasting, and manufacturing process control. The research will involve making advances in the areas of statistical theory, algorithm design, and high performance computing.

弗里德曼9704431 预测学习系统是一种计算机程序，其构造用于在给定真实的系统的其他属性（“输入”）的值的情况下预测该系统的某些属性（“输出”）的值的规则。预测学习系统试图纯粹通过处理从过去成功解决的案例中获取的数据来构建或“学习”有用的预测规则;也就是说，已经确定输出和输入变量的值的案例。 学习算法的作用是自动提取和组织这些数据中的信息，以获得准确的规则，用于预测只有输入变量的值已知的新情况的输出值。 本研究集中在预测学习方法的发展与过去的方法完全不同的新范式。 目标是产生更快更强大的学习算法。这些新算法可以应用于解决更广泛的更复杂的问题，例如通信，生物技术，环境预测和制造过程控制领域中出现的问题。 该研究将涉及统计理论，算法设计和高性能计算领域的进展。