Boosting: Foundations and Algorithms (Robert E. Schapire, et al)

 
0.0 (0)
Boosting: Foundations and Algorithms (Robert E. Schapire, et al)

Boosting is a method of machine learning that builds extremely accurate predictors by combining a variety of ineffective "rules of thumb." With ties to a variety of subjects, such as statistics, game theory, convex optimization, and information geometry, boosting has developed a fairly complex theory.

Boosting algorithms have achieved practical success in a variety of industries, including biology, vision, and speech recognition. Boosting has been viewed as mysterious, contentious, and even paradoxical at various points over its history.

This book, written by the method's creators, compiles, arranges, streamlines, and significantly expands two decades of boosting research. It does this by presenting both theory and applications in a way that is understandable to readers from different backgrounds while also serving as an authoritative source for researchers at a more advanced level. The book is suitable for course use as well because it introduces all the information and includes activities in every chapter.

The book starts with a general introduction to machine learning algorithms and their analysis, explores the core theory of boosting, particularly its ability to generalize, looks at some of the countless other theoretical stances that help to explain and understand boosting, offers practical boosting extensions for more complex learning problems, and presents a number of advanced theoretical topics. There are numerous examples and applications provided throughout.

Ebook Details

About the Authors
Princeton University's Robert E. Schapire teaches computer science there. Professor of Computer Science Yoav Freund teaches at the University of California, San Diego. Freund and Schapire were honored with the Kanellakis Theory and Practice Award in 2004 and the Gödel Prize in 2003 for their work on boosting.
Publisher
Published
Published Date / Year
(January 10, 2014)
License(s)
MIT Open Access License
Hardcover
544 pages
eBook Format
HTML and PDF
Language
English
ISBN-10
0262526034
ISBN-13
978-0262526036

Similar Programming & Computer Books

Éléments d'algorithmique - Algorithmic elements (D. Beauquier, et al)
This free programming book differs from other treatises on algorithms in two ways: first, we give special attention to the new tree structures that have emerged recently (bicolor trees,...
Complexité algorithmique - Algorithmic complexity (Sylvain Perifel)
The foundational ideas of algorithmic complexity theory are first covered in this free programming book before moving on to a number of more sophisticated subjects. ...
Algorithmique du texte - Text Algorithms (Maxime Crochemore, et al)
This free programming book offers a broad overview of text-processing algorithms. As such, it is an algorithmic book, but one whose goal is to utilize computers to manipulate language....
Strategic Foundations of General Equilibrium: Dynamic Matching and Bargaining Games (Douglas Gale)
Since Adam Smith's day, the theory of competition has played a significant role in economic study. This book, published by one of the most eminent modern economic theorists, details...
The Pure Logic Of Choice (Richard D. Fuerle)
A broad theory of economics based on free will is presented in this free programming book. The assumption that humans have free will and the ability to alter physical...
Portfolio Theory and Financial Analyses (Robert Alan Hill)
Whether they involve calculating the return on a portfolio, analyzing portfolio risk, or assessing the effectiveness of the portfolio management process, this free programming book links each of the...
Price Theory: An Intermediate Text (David D. Friedman)
In order to help the reader grasp the economic way of thinking, the author first gives verbal, intuitive explanations of the topics before using graphs and/or calculus to illustrate...
Mathematical Models in Portfolio Analysis (Farida Kachapova)
This free programming book presents the mathematical theory of portfolio modeling in financial mathematics as a coherent whole, with justifications for each step. ...
Stochastic Calculus and Finance (Steven E. Shreve)
The first 10 years of the Carnegie Mellon Professional Master program in Computational Finance led to the development of stochastic calculus for finance. Students with calculus and probability based...
Math for Trades: Volume 1 (Chad Flinn, et al.)
The foundational elements for learning math are presented in this volume. Whole numbers, fractions, decimals, and percents are all included in the book. ...

Others Programming Books by The MIT Press

Cellular: An Economic and Business History of the International Mobile-Phone Industry (Daniel D. Garcia-Swartz, et al)
From the late 1970s to the present, charts the development of the global cellular industry. It took exceptional collaboration between businesses, governments, and industrial sectors for the mobile phone...
The Ecology of Games: Connecting Youth, Games, and Learning (Katie Salen)
Little has been published on an overall "ecology" of gaming, game design, and play - mapping the ways that all the various elements, from code to social practices to...
Categories, Types, and Structures: An Introduction to Category Theory for the Working Computer Scientist (Andrea Asperti, et al)
This free programming book offers an accessible introduction to category theory for computer scientists as well as useful examples in the context of programming language design. In "Categories, Types...
Sheaf Theory through Examples (Daniel Rosiak)
This free programming book offers a clear introduction to elementary sheaf theory from the standpoint of applied category theory and explores several applications, such as n-colorings of graphs, satellite...
Wandering Games (Melissa Kagen)
Games may use wandering as a topic, formal style, metaphor for aesthetics, or player action. It can refer to moving forward, moving backward, traveling, meandering, or escaping. ...
Probabilistic Machine Learning: Advanced Topics (Kevin Patrick Murphy)
In this book, we broaden the use of machine learning to more difficult issues.
Introduction to Online Convex Optimization (Elad Hazan)
In this book, optimization is portrayed as a procedure. It is not realistic to draw out a thorough theoretical model and utilize traditional algorithmic theory and/or mathematical optimization in...
Statistical Mechanics of Lattice Systems: A Concrete Mathematical Introduction (Sacha Friedli, et al)
Using a variety of specific models, such as the Curie-Weiss and Ising models, the Gaussian free field, O(n) models, and models with Ka interactions, this inspiring textbook provides a...
Software Design for Flexibility: How to Avoid Programming Yourself into a Corner (Chris Hanson, et al)
Techniques for designing huge systems that are easily reconfigurable for different scenarios with very modest programming changes.
Global Fintech: Financial Innovation in the Connected World (David L. Shrier, et al.)
The global financial services industry has been completely transformed by artificial intelligence, big data, blockchain, and other new technologies, opening up new prospects for business owners and corporate innovators....
Structure and Interpretation of Computer Programs, JavaScript Edition (Harold Abelson, et al.)
By building a number of mental models for computation, this book introduces the reader to the fundamental concepts of computation.
The New Hacker's Dictionary (The Jargon File) by Eric S. Raymond
This page includes a glossary of terminology used by various computer hacker subcultures. What we describe here is the language hackers use among themselves for amusement, social contact, and...
Algorithms for Decision Making (Mykel Kochenderfer, et al)
In this book, algorithms for making decisions in the face of uncertainty are introduced in great detail. It introduces the underlying mathematical problem formulations and the strategies for addressing...
Machine Learning: A Probabilistic Perspective (Kevin Patrick Murphy)
Automated data analysis techniques are necessary given the Web-enabled flood of electronic data we face today. These are provided by machine learning, which creates techniques that can automatically find...
Exploratory Programming for the Arts and Humanities (Nick Montfort)
There are no prerequisites or assuming prior programming experience in this book, which introduces programming to readers interested in the arts and humanities.
The Constitution of Algorithms: Ground-Truthing, Programming, Formulating (Florian Jaton)
The technologies we use every day are powered by algorithms, which are sometimes used interchangeably with words like "big data," "machine learning," and "artificial intelligence." Arguments concerning the real...
Linguistics for the Age of AI (Marjorie McShane, et al)
This book presents a model of language understanding for intelligent agent systems that is human-inspired and linguistically complex.
Probabilistic Machine Learning: An Introduction (Kevin Patrick Murphy)
Using probabilistic models and inference as a unifying strategy, this book provides a thorough introduction to machine learning.
How Humans Judge Machines (Cesar A. Hidalgo, et al)
A thorough analysis of how individuals respond to human activities versus machine actions. This book investigates when and why people differentiate between humans and machines through dozens of tests....
Certified Programming with Dependent Types: A Pragmatic Introduction to the Coq Proof Assistant (Adam Chlipala)
Many different computer science research endeavors can benefit from the use of mechanized program verification technologies, and the use of similar formal proof-checking tools in mathematics and engineering is...

User reviews

There are no user reviews for this listing.
Ratings
Rate this Book
Comments