We describe a new Boosting algorithm which combines the base hypotheses with symmetric functions. Among its properties of practical relevance, the algorithm has significant resistance against noise, and is efficient even in an agnostic learning setting. This last property is ruled out for voting-based Boosting algorithms like AdaBoost. Experiments carried out on thirty domains, most of which readily available, tend to display the reliability of the classifiers built.