In classical statistics, the goal is often to find the parameters that best fit a known model. In SLT, the model itself is often unknown. The theory distinguishes between (the error on the training data) and Expected Risk (the error on future, unseen data).
A mechanism that provides the "target" or output value for each input vector. The Nature of Statistical Learning Theory
One of the most profound contributions of SLT is the concept of (Vapnik-Chervonenkis dimension). This provides a formal way to measure the "capacity" or flexibility of a learning machine. Unlike traditional methods that rely on the number of parameters, the VC dimension measures the complexity of the functions the machine can implement. In classical statistics, the goal is often to
A source of data that produces random vectors, usually assumed to be independent and identically distributed (i.i.d.). A mechanism that provides the "target" or output