New Directions in Predictive Learning for Classification

弗里德曼9704431预测学习系统是一种计算机程序，它构造规则，用于在给定实际系统的某些属性(输入)的值的情况下预测该系统的某些属性(输出)的值。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得的数据，即输出变量和输入变量的值都已确定的案例，来构建或“学习”有用的预测规则。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，用于预测只知道输入变量的值的新案例的输出值。这项研究集中在发展预测性学习方法的新范式上，与过去的方法截然不同。其目标是产生更快、更强大的学习算法。然后，这些新的算法可以应用于解决更广泛类别的更复杂的问题，例如在通信、生物技术、环境预测和制造过程控制领域中出现的问题。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.