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A collection of papers produced on the theory of boosting as applied to binary classification. Further extensions to the multi-class classification problem and necessary and sufficient conditions to ensure boostability i.e. weak learning conditions. Finally, an overview over boosting algorithms and models employed in industry.

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Boosting and Boostability: Weak Learning Implies Strong Learning

In this paper, we explore the design and analysis of boosting algorithms and the necessary and sufficient conditions on the weak learners to ensure boostability. Following a literature survey on the computational learning theory behind boosting, we explore boosting, boostability, and conditions for both in several boosting algorithms. Finally, we discuss the application of such algorithms in the binary classification problem and extensions to the multi-class problem.

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A collection of papers produced on the theory of boosting as applied to binary classification. Further extensions to the multi-class classification problem and necessary and sufficient conditions to ensure boostability i.e. weak learning conditions. Finally, an overview over boosting algorithms and models employed in industry.

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