Short definition of boosting:
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 (it can label examples better than random guessing).
Short definition of Random Forest:
Random Forests grows many classification trees. To classify a new object from an input vector, put the input vector down each of the trees in the forest. Each tree gives a classification, and we say the tree "votes" for that class. The forest chooses the classification having the most votes (over all the trees in the forest).
Another short definition of Random Forest:
A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.
As I understand Random Forest is an boosting algorithm which uses trees as its weak classifiers. I know that it also uses other techniques and improves upon them. Somebody corrected me that Random Forest is not a boosting algorithm?
Can someone elaborate upon this, why Random Forest is not a boosting algorithm?