FD Project

Boosting is a machine learning meta-algorithm for performing supervised learning. Boosting is based on the question posed by Kearns: can a set of weak learners create a single strong learner ? A weak learner is defined to be a classifier which is only slightly correlated with the true classification. In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.

The affirmative answer to Kearns' question has significant ramifications in machine learning and statistics.

Взято из wikipedia. Там же есть первичные ссылки. Дальше берем Google и работаем ))).

Метки: AdaBoost, Kearns