Introduction to Boosting

author: Gunnar Rätsch, Max Planck Institute
published: Feb. 25, 2007,   recorded: August 2006,   views: 2410
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Description

This course provides an introduction to theoretical and practical aspects of Boosting and Ensemble Learning. I will begin with a short description of the learning theoretical foundations of weak learners and their linear combination. Then we point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broader spectrum of problems. In the course we will discuss "tricks of the trade", algorithmic issues, experimental results and a few applications.

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Reviews and comments:

Comment1 Demo, December 12, 2018 at 1:41 p.m.:

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance, bias!!
https://www.demolink123.com

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