Flexible and Adaptive Statistical Modeling

9971405在20世纪80年代，试图在大脑中建模学习的研究人员为多层非线性神经网络开发了几种新颖的学习算法。 顺便说一句，这些算法被证明是自适应回归和分类的非常强大的技术，并且已经证明了它们的有用性，而不管它们是否是大脑的良好模型。 它们现在被应用于医疗诊断，化学过程控制，形状识别和其他广泛的重要实际问题。 与此同时，统计领域的自适应技术也取得了重大进展。 新的统计方法比线性回归和线性判别分析等经典方法更强大。 最近开发的一些程序包括CART（分类和回归树），广义加性模型，MARS（多元加性回归样条）和最近邻算法的复杂版本，这些算法为输入空间学习适当的度量。 虽然它们来自不同的领域，使用不同的术语，但这些方法有很多共同之处。 该提案中的一项内容是编写一本研究专著，旨在将这些想法集中在一起，以统一的方式加以解释。 另外两个项目探讨了最近改进分类器和自适应模型选择的一些新方法。这项工作旨在开发基于历史数据进行预测的新模型和方法。 这些技术在许多不同的领域都很重要，包括医疗诊断、财务预测和工业过程控制。 生物技术领域是这些方法的一个特别重要的应用。 科学家们现在有技术可以同时测量数千个基因的基因表达水平，从而有可能确定哪些人类基因与癌症和心脏病等疾病有关。 对大量信息进行分类就像试图在干草堆中找到一根针，而像这里研究的预测方法将为这种搜索提供重要工具。

9971405During the 1980's, researchers who were trying to model learning in the brain developed several novel learning algorithms for multilayer non-linear neural networks. Somewhat incidentally, these algorithms turn out to be very powerful techniques for adaptive regression and classification, and have proven their usefulness independent of whether or not they are a good model for the brain. They are now being applied to medical diagnosis, chemical process control, shape recognition and a wide range of other important practical problems. At the same time, there have been significant advances in adaptive techniques in the field of statistics. The new statistical methods are more powerful than classical techniques such as linear regression and linear discriminant analysis. Some of the recently developed procedures include CART (Classification And Regression Trees), generalized additive models, MARS (Multivariate Additive Regression Splines), and sophisticated versions of nearest neighbor algorithms that learn an appropriate metric for the input space. Although they come from different fields using different terminologies, these methods have much in common. One item in this proposal is a research monograph that seeks to bring many of these ideas under one umbrella, explaining them in a unified fashion. Two other items explore some recent new methods for improving classifiers and for adaptive model selection.This work aims at developing new models and methods for making predictions based on historical data. These techniques are important in many different fields including medical diagnosis, financial forecasting and industrial process control. The area of biotechnology is an especially important application for these methods. Scientists now have techniques for measuring gene expression levels for thousands of genes at the same time, allowing the exciting possibility of determining which human genes are involved in a diseases such as cancer and heart disease. Sorting through the mass of information is like trying to find a needle in a haystack, and predictive methods like the ones studied here will provide an important tool in this search.