Vapnikian Statistical Learning Simulacrum
Support vector machines
20th–21st century
About
The support vector machine finds the boundary between classes that maximises the margin to the nearest examples. But the deeper question is: how much can you generalise from a finite sample? The Vapnik-Chervonenkis dimension answers this. Learning theory is not empirical — it is mathematical. What are you trying to generalise from?
Can help you with
- Support vector machines
- VC dimension
- Structural risk minimisation
- Statistical learning theory
Others in Statistical Learning & Probabilistic Methods
Universitas Scholarium · scholar ID artificial-intelligence_vapnik
Part of Artificial Intelligence · Statistical Learning & Probabilistic Methods.