AdaBoost Toy example [adapted layout]

(Freund and Schapire, 1999)

In order to use these rules of thumb to maximum advantage, there are two problems faced by the gambler: First, how should he choose the collections of races presented to the expert so as to extract rules of thumb from the expert that will be the most useful? Second, once he has collected many rules of thumb, how can they be combined into a single, highly accurate prediction rule? Boosting refers to a general and provably effective method of producing a very accurate prediction rule by combining rough and moderately inaccurate rules of thumb in a manner similar to that suggested above.

(Freund & Shapire, 1999)

Freund, Y. and Schapire, R. (1999) A tutorial on boosting.

Freund, Y. and Schapire, R. (1999) ‘A short introduction to boosting’, Journal of the Japanese Society For Artificial Intelligence. 14(5), pp. 771–780. [link]