Title: The Elements of Statistical Learning Data Mining, Inference and Prediction
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Press: Springer Verlag
EISBN: 9780387848587
PISBN: 9780387848570
Edition: 2nd
Introduction: This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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.
Digital Access
Full-text Availability: Springer
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Physical Copy Information
Location: International Campus Library - Textbook Shelf
Call Number: TP181/LH1.2-2/ZJUI
Institute: ZJUI
Major: EE/ ECE
Course ID: ECE 365/CS 412
Course Title: Data Science and Engineering/Introduction to Data Mining