The Nature of Statistical Learning Theory

Formalized Statistical Learning Theory, Vapnik-Chervonenkis (VC) dimension, Structural Risk Minimization, and Support Vector Machines.

Provides the theoretical foundation for maximum-margin classifiers, regularization penalties, and generalization bounds.

Assumptions

• Data generated i.i.d. from unknown fixed probability distribution
• Maximizing geometric margin minimizes true generalization error bound

Limitations

• VC bounds can be loose in practice for deep non-linear neural networks
• Quadratic programming scales quadratically with dataset size