Data Mining, Inference, and Prediction, Second Edition Trevor Hastie is an in-depth textbook on data mining and statistical prediction methods. This major new edition introduces many new topics such as graphical models, random forests, and ensemble methods. The book is suitable for professionals and researchers who work with data analysis in various sectors.
Contents
This reference work provides a coherent overview of many topics in data mining, ranging from supervised learning to unsupervised learning. In addition to classical methods, it also covers, among other things, neural networks, support vector machines, classification trees, and boosting—the first complete overview of boosting in one publication.
The major new edition has been enhanced with, among other things, least angle regression, path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. It also includes attention to methods for 'wide' data, focusing on multiple testing and false discovery rates.
Product specifications
- Author: Trevor Hastie
- Publisher: Springer-Verlag New York Inc.
- Publication date: 2009-02-09
- Number of pages: 745
- ISBN: 9780387848570
- Theme: Artificial intelligence (AI)
About the author
Trevor Hastie, together with Robert Tibshirani and Jerome Friedman, is a leading expert in statistics and data mining at Stanford University. Their collaborative work includes groundbreaking methods such as generalized additive models, the lasso, and various data-mining tools such as CART and gradient boosting.