Support Vector Machines (SVMs) are a new classification technique which has 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 p-norm instead of the Euclidean norm, that is. When p = 1, for example, it is a linear programming problem with a much lower computational load. In this article, we theoretically show that p has very little affect on the generalization performance of SVMs in practice by considering its geometrical meaning.
|ジャーナル||Midwest Symposium on Circuits and Systems|
|出版ステータス||Published - 2004 12月 1|
|イベント||The 2004 47th Midwest Symposium on Circuits and Systems - Conference Proceedings - Hiroshima, Japan|
継続期間: 2004 7月 25 → 2004 7月 28
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