Statistical learning theory provides the theoretical foundation for machine learning, explaining how algorithms learn from data. Vapnik's 'The Nature of Statistical Learning Theory' introduces key concepts like VC dimension, structural risk minimization, and support vector machines. This theory underpins many modern ML algorithms and helps understand generalization.
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