Boosting: Foundations and Algorithms by Schapire & Freund
Boosting: Foundations and Algorithms - Table of Contents
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression
4. Linear Methods for Classification
5. Basis Expansions and Regularization
6. Kernel Smoothing Methods
7. Model Assessment and Selection
8. Model Inference and Averaging
9. Additive Models, Trees, and Related Methods
10. Boosting and Additive Trees
11. Neural Networks
12. Support Vector Machines and Flexible Discriminants
13. Prototype Methods and Nearest-Neighbors
14. Unsupervised Learning
15. Random Forests
16. Ensemble Learning
17. Undirected Graphical Models
18. High-Dimensional Problems: p » N
What You Will Learn in Boosting: Foundations and Algorithms
"Boosting: Foundations and Algorithms" by Robert E. Schapire, Yoav Freund is a comprehensive guide to the "boosting" technique in "machine learning". Boosting is an ensemble method that combines multiple "weak learners" to create a single, strong predictive model. The book clearly explains the core concepts, starting from the basics of predictive modeling to advanced boosting strategies, making it ideal for both "researchers" and practitioners.
The authors dive into the "theoretical foundations" of boosting, including AdaBoost and margin theory, while also connecting ideas from "optimization" and "statistics". They show why boosting performs so well in practice, providing mathematical intuition and detailed algorithmic explanations. Each chapter includes examples and exercises, helping readers understand how boosting can improve model accuracy and generalization on real-world datasets.
Beyond theory, the book explores extensions like multiclass classification, ranking, and online learning, offering a practical perspective for applying boosting in diverse scenarios. Written by the original pioneers of the method, it blends rigorous "mathematics" with hands-on insights. This makes it a key resource for anyone looking to master boosting, understand ensemble methods deeply, and advance their machine learning skills.
Book Details & Specifications
Title:
Boosting: Foundations and Algorithms by Schapire & Freund
Publisher:
The MIT Press
Year:
2012
Pages:
544
Type:
PDF
Language:
English
ISBN-10 #:
0262526034
ISBN-13 #:
978-0262526036
License:
CC BY-NC-ND 4.0
Amazon:
Amazon
About the Author: Robert E. Schapire, Yoav Freund
The author Robert E. Schapire, Yoav Freund
are pioneering "machine learning" researchers, best known for developing "boosting" algorithms. Freund, born in Israel, earned his Ph.D. from the University of California, Santa Cruz, and is a professor at UC San Diego. Schapire, an American computer scientist, completed his Ph.D. at MIT and worked at Microsoft Research. Both have deep expertise in "ensemble learning", algorithm design, and "data science", shaping modern predictive modeling. Their joint work on "AdaBoost" revolutionized "predictive analytics", enabling weak learners to form strong classifiers. They received top honors like the Gödel Prize and Paris Kanellakis Award, influencing AI and "statistical learning" research worldwide.
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