|Machine learning and Data mining|
|Supervised learning (classification, regression)|
Vapnik–Chervonenkis theory (also known as VC theory) was developed during 1960–1990 by Vladimir Vapnik and Alexey Chervonenkis. The theory is a form of computational learning theory, which attempts to explain the learning process from a statistical point of view.
VC theory covers at least four parts (as explained in The Nature of Statistical Learning Theory):
- Theory of consistency of learning processes
- What are (necessary and sufficient) conditions for consistency of a learning process based on the empirical risk minimization principle?
- Nonasymptotic theory of the rate of convergence of learning processes
- How fast is the rate of convergence of the learning process?
- Theory of controlling the generalization ability of learning processes
- How can one control the rate of convergence (the generalization ability) of the learning process?
- Theory of constructing learning machines
- How can one construct algorithms that can control the generalization ability?
The last part of VC theory introduced a well-known learning algorithm: the support vector machine.
- ^ Vapnik, Vladimir N (2000). The Nature of Statistical Learning Theory. Information Science and Statistics. Springer-Verlag. ISBN 978-0-387-98780-4.
- Vapnik, Vladimir N (1989). Statistical Learning Theory. Wiley-Interscience. ISBN 0-471-03003-1.
- See references in articles: Richard M. Dudley, empirical processes, Shattered set.