Effects of Norms on Learning Properties of Support Vector Machines
説明
Support vector machines (SVMs) are known to have a high generalization ability, yet a heavy computational load since margin maximization results in a quadratic programming problem. It is known that this maximization task results in a pth-order programming problem if we employ the L/sub P/ norm instead of the L/sub 2/ norm. In this paper, we theoretically show the effects of p on the learning properties of SVMs by clarifying its geometrical meaning.
収録刊行物
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- Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
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Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. 5 241-244, 2006-10-04
IEEE