6883e621_ws___Machine_Learning__Theory___3
[0] Boosting [1] You create a learning algorithm that may be imperfect but which has some predictive edge, then apply it repeatedly in various ways to make a final predictor. [2] A focus on getting something that works quickly is natural. This approach is relatively automated and (hence) easy to apply for beginners. [3] 
The boosting framework tells you nothing about how to build that initial algorithm. The weak learning assumption becomes violated at some point in the iterative process.
All Models of Learning have Flaws « Machine Learning (Theory)
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