no code implementations • 27 Oct 2014 • Jayadeva, Sanjit S. Batra, Siddharth Sabharwal
For a linear hyperplane classifier in the input space, the VC dimension is upper bounded by the number of features; hence, a linear classifier with a small VC dimension is parsimonious in the set of features it employs.
no code implementations • 16 Oct 2014 • Jayadeva, Suresh Chandra, Siddarth Sabharwal, Sanjit S. Batra
The capacity of a learning machine is measured by its Vapnik-Chervonenkis dimension, and learning machines with a low VC dimension generalize better.