The most famous practical outcome of this theory is the Support Vector Machine (SVM). Rather than just minimizing training error, SVMs are designed to maximize the "margin" between classes. This approach directly implements the theoretical findings of SLT, ensuring that the chosen model has the best possible guarantee of generalizing to new information.
A set of functions (the hypothesis space) from which the machine selects the best candidate to approximate the supervisor. The Nature of Statistical Learning Theory
A source of data that produces random vectors, usually assumed to be independent and identically distributed (i.i.d.). The most famous practical outcome of this theory