A source of data that produces random vectors, usually assumed to be independent and identically distributed (i.i.d.).
A measure of the discrepancy between the machine’s prediction and the actual output. The Problem of Generalization The Nature of Statistical Learning Theory
Statistical learning theory (SLT) provides the theoretical foundation for modern machine learning, shifting the focus from simple data fitting to the fundamental challenge of . Developed largely by Vladimir Vapnik and Alexey Chervonenkis, the theory seeks to answer a primary question: Under what conditions can a machine learn from a finite set of observations to make accurate predictions about data it has never seen? The Core Framework A source of data that produces random vectors,
A mechanism that provides the "target" or output value for each input vector. The Nature of Statistical Learning Theory