Since the coined question in 1986 “Can a set of weak learners create a single strong learner?”, ensemble learning has been focused on merging simple machine learning methods in order to increase predictive performance. Characteristics such as stability and diversity are important to choose these weak learners in bagging procedure. In the same field, Support Vector Models (SVM) are strong and stable learners which have been drawing the attention of the community once these models have some properties which are easy to characterize and at the same time provide an estimation process with global optimization properties. In this talk, we present the Random Machines method. A new machine learning method based on SVM ensemble learning which exposes how a set of strong learners can create a single stronger learner.