Statistical Learning Theory

Statistical learning theory is the broad framework for studying the concept of inference in both supervised and unsupervisedmachine learning. Inference covers the entire spectrum of machine learning, from gaining knowledge, making predictions or decisions and constructing models from a set of labeled or unlabeled data. The entire process is stated in a statistical framework, with every assumption stated mathematically as a null or alternative hypothesis. The practical goals of this approach are to make machine learning more precise (reliably reproduceable) and to create new or improved modeling algorithms. This is primarily accomplished by providing a formal, statistical definition of abstract concepts, like learning, generalization, overfitting and performance, then testing these hypotheses one parameter at a time. But in statistical machine learning, the entire process needs to be automated for a computer program to learn from it.