14 citations · 21 across the 3 of their papers we have counts for
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Learning a Fuzzy Hyperplane Fat Margin Classifier with Minimum VC dimension
Jayadeva, Sanjit Singh Batra, Siddarth Sabharwal
The Vapnik-Chervonenkis (VC) dimension measures the complexity of a learning machine, and a low VC dimension leads to good generalization. The recently proposed Minimal Complexity…
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…
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…