35 citations · 135 across the 19 of their papers we have counts for
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cs.LG2014★ 4 cited
Feature Selection through Minimization of the VC dimension
Jayadeva, Sanjit S. Batra, Siddharth Sabharwal
Feature selection involes identifying the most relevant subset of input features, with a view to improving generalization of predictive models by reducing overfitting. Directly sea…
cs.LG2014★ 14 cited
Learning a hyperplane regressor by minimizing an exact bound on the VC dimension
Jayadeva, Suresh Chandra, Siddarth Sabharwal +1
The capacity of a learning machine is measured by its Vapnik-Chervonenkis dimension, and learning machines with a low VC dimension generalize better. It is well known that the VC d…
cs.LG2014★ 35 cited
Learning a hyperplane classifier by minimizing an exact bound on the VC dimension
Jayadeva
The VC dimension measures the capacity of a learning machine, and a low VC dimension leads to good generalization. While SVMs produce state-of-the-art learning performance, it is w…