New Directions in Predictive Learning for Classification

预测学习系统是一种计算机程序，它根据实际系统（“输出”）的其他属性（“输入”）的值，构建预测该系统某些属性值的规则。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，输出和输入变量的值都已经确定的情况。学习算法的作用是自动提取和组织这些数据中的信息，以获得准确的规则来预测只有输入变量的值的新情况的输出值。本研究集中于发展预测学习方法论的新范例，与过去的方法截然不同。目标是产生更快更强大的学习算法。然后，这些新算法可以应用于解决更广泛、更复杂的问题，例如在通信、生物技术、环境预测和制造过程控制等领域出现的问题。这项研究将涉及在统计理论、算法设计和高性能计算领域取得进展。

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.