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).
At its heart, the nature of statistical learning is defined by four essential components: The Nature of Statistical Learning Theory
A set of functions (the hypothesis space) from which the machine selects the best candidate to approximate the supervisor. In classical statistics, the goal is often to
A mechanism that provides the "target" or output value for each input vector. At its heart, the nature of statistical learning
A measure of the discrepancy between the machine’s prediction and the actual output. The Problem of Generalization
The "nature" of this field is essentially the study of the gap between these two. If a model is too simple, it fails to capture the data's structure (underfitting). If it is too complex, it "memorizes" the noise in the training set (overfitting), leading to low empirical risk but high expected risk. Capacity and the VC Dimension
SLT proves that for a machine to generalize well, its capacity must be controlled relative to the amount of available training data. This led to the principle of , which balances the model's complexity against its success at fitting the training data. From Theory to Practice: Support Vector Machines