Logo

Boosting: Foundations and Algorithms

Large book cover: Boosting: Foundations and Algorithms

Boosting: Foundations and Algorithms
by

Publisher: The MIT Press
ISBN-13: 9780262310413
Number of pages: 544

Description:
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry.

Home page url

Download or read it online for free here:
Read online
(online html)

Similar books

Book cover: Information Theory, Inference, and Learning AlgorithmsInformation Theory, Inference, and Learning Algorithms
by - Cambridge University Press
A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.
(12408 views)
Book cover: Statistical Learning and Sequential PredictionStatistical Learning and Sequential Prediction
by - University of Pennsylvania
This text focuses on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. We will discuss learning algorithms...
(888 views)
Book cover: An Introductory Study on Time Series Modeling and ForecastingAn Introductory Study on Time Series Modeling and Forecasting
by - arXiv
This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.
(4488 views)
Book cover: Modeling Agents with Probabilistic ProgramsModeling Agents with Probabilistic Programs
by - AgentModels.org
This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.
(475 views)