Basic understanding of the Adaboost algorithm
adaboost, machine-learning
Solution
A classifier is a black box that receives an input (feature vectors) and returns an output (labeled vectors). So to call something a classifier, you only care about what it does, and not how it does it. AdaBoost's classifier can be seen as such black box, so it's indeed a single classifier, even if it uses internally several weak classifiers to produce such output.
Problem
I'm a machine learning newbie trying to understand how Adaboost works. I've read many articles explaining how Adaboost makes use of set of weak *classifiers* to create a strong classifier. However, I seem to have problem understanding the statement that "Adaboost creates a Strong Classifier". When I looked at implementations of Adaboost, I've realized that it doesn't "actually" create a Strong Classifier but somehow in the TESTING PHASE figures out on "how to use set of Weak Classifiers to get more accurate results" which in turn acts like a strong classifier "Collectively". So technically there is NO SINGLE STRONG CLASSIFIER created (but set of weak classifiers collectively act as a strong classifier). Please correct me if I'm wrong. It would be nice if someone can throw in some comments regarding this.