Learning a hyperplane regressor by minimizing an exact bound on the VC dimension

16 Oct 2014JayadevaSuresh ChandraSiddarth SabharwalSanjit 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. It is well known that the VC dimension of SVMs can be very large or unbounded, even though they generally yield state-of-the-art learning performance... (read more)

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