The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2/e (Hardcover)

Trevor Hastie, Robert Tibshirani, Jerome Friedman

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商品描述

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

商品描述(中文翻譯)

在過去的十年中,計算和資訊技術爆炸性增長。隨之而來的是在醫學、生物學、金融和市場營銷等領域中出現了大量的數據。理解這些數據的挑戰促使統計學領域開發了新的工具,並產生了數據挖掘、機器學習和生物信息學等新領域。許多這些工具具有共同的基礎,但常常使用不同的術語來表達。本書以共同的概念框架描述了這些領域的重要思想。雖然方法是統計學的,但重點是概念而不是數學。書中提供了許多例子,並廣泛使用彩色圖形。對於統計學家和對科學或工業中的數據挖掘感興趣的人來說,這是一個寶貴的資源。本書的範圍很廣,從監督學習(預測)到非監督學習。其中許多主題包括神經網絡、支持向量機、分類樹和增強學習,這是任何書籍中首次全面涵蓋這個主題的。

這本重要的新版涵蓋了原版中未涉及的許多主題,包括圖形模型、隨機森林、集成方法、最小角度回歸和套索的路徑算法、非負矩陣分解和譜聚類。還有一章介紹了處理“寬”數據(p大於n)的方法,包括多重檢驗和虛假發現率。