A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

Taylor Arnold, Michael Kane, Bryan W. Lewis

A Computational Approach to Statistical Learning (Chapman & Hall/CRC Texts in Statistical Science)

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A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.

 

The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.

 

Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015.

 

Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010.

 

Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

商品描述(中文翻譯)

《統計學習的計算方法》通過關注流行統計方法背後的算法和數值動機，提供了對預測建模的新穎介紹。該書包含了80多個原始參考函數的註釋代碼。這些函數提供了常見統計學習算法的最小工作實現。每一章的結尾都有一個完整的應用案例，用一個真實世界的數據集來演示預測建模任務。

該書首先詳細分析了線性模型和最小二乘法。隨後的章節探討了擴展技術，如岭回歸、廣義線性模型和加法模型。下半部分則專注於使用通用算法進行凸優化以及其在統計學習任務中的應用。涵蓋的模型包括彈性網、密集神經網絡、卷積神經網絡（CNN）和譜聚類。整本書的統一主題是在描述預測模型時使用優化理論，特別關注奇異值分解（SVD）。通過這一主題，計算方法激發並澄清了各種預測模型之間的關係。

泰勒·阿諾德（Taylor Arnold）是里士滿大學統計學助理教授。他在計算機視覺、自然語言處理和數字人文學交叉領域的工作得到了國家人文基金會（NEH）和美國學會（ACLS）的多項資助。他的第一本書《R中的人文數據》於2015年出版。

邁克爾·凱恩（Michael Kane）是耶魯大學生物統計學助理教授。他獲得了美國國家衛生研究院（NIH）、DARPA和比爾及梅琳達·蓋茨基金會的資助。他的R軟件包《bigmemory》於2010年獲得了統計軟件的Chamber獎。

布萊恩·路易斯（Bryan Lewis）是應用數學家，也是許多熱門R軟件包的作者，包括《irlba》、《doRedis》和《threejs》。

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