Statistical Learning as a Regression Problem
Statistical Learning as a Regression Problem
复制标题
DOI：
10.1007/978-0-387-77501-2_1
复制
发表时间：
2008-01-01
期刊：
影响因子：
--
通讯作者：
Berk, Richard A.
中科院分区：
文献类型：
--
作者：
Berk, Richard A.
作为第一近似，人们可以将统计学习视为探索性数据分析（EDA）的“肌肉车”版本。就像在EDA中一样，数据以相对较少的先验信息进行处理，并以高度归纳的方式进行检查。知识发现可以是一个关键目标。但由于过去二十年来计算能力和计算机算法的巨大发展，提取以前无法获取的信息成为可能。此外，由于统计学习已经在许多不同的学科中发展，其目标和方法比传统的EDA更加多样化。在本书中，重点是可以在回归框架内理解的统计学习过程。对于各种各样的应用，这将不会造成重大的限制，并将极大地促进说明。负责大多数统计学习技术的统计学、应用数学和计算机科学的研究人员经常使用自己独特的术语，并倾向于给他们的产品贴上可爱但有点模糊的标签：装袋、提升、捆绑、随机森林、套索等等。缩写词也被广泛使用：CART、MARS、MART、LARS等等。回归框架提供了一个方便和指导性的结构，在其中这些过程可以更容易地理解。
As a first approximation, one can think of statistical learning as the “muscle car” version of Exploratory Data Analysis (EDA). Just as in EDA, the data are approached with relatively little prior information and examined in a highly inductive manner. Knowledge discovery can be a key goal. But thanks to the enormous developments in computing power and computer algorithms over the past two decades, it is possible to extract information that would have previouslybeen inaccessible. In addition, because statistical learning has evolved in a number of different disciplines, its goals and approaches are far more varied than conventional EDA.In this book, the focus is on statistical learning procedures that can be understood within a regression framework. For a wide variety of applications, this will not pose a significant constraint and will greatly facilitate the exposition. The researchers in statistics, applied mathematics and computer science responsible for most statistical learning techniques often employ their own distinct jargon and have a penchant for attaching cute, but somewhat obscure, labels to their products: bagging, boosting, bundling, random forests, the lasso, and others. There is also widespread use of acronyms: CART, MARS, MART, LARS, and many more. A regression framework provides a convenient and instructive structure in which these procedures can be more easily understood.