Feature Selection through Minimization of the VC dimension

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.

Drug Discovery feature selection +1