An Efficient Method for Simplifying Decision Functions of Support Vector Machines
An Efficient Method for Simplifying Decision Functions of Support Vector Machines
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DOI：
10.1093/ietfec/e89-a.10.2795
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
Jun Guo;Norikazu Takahashi;T. Nishi
中科院分区：
文献类型：
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作者：
Jun Guo;Norikazu Takahashi;T. Nishi
本文提出了一种简化支持向量机（SVM）决策函数的新方法。在我们的方法中，首先通过使用所有训练样本以通常的方式确定决策函数。接下来，那些对决策函数贡献较小的支持向量被从训练样本中排除。最后利用剩余样本得到新的决策函数。实验结果表明，该方法能够有效简化SVM的决策函数，且不降低泛化能力。
A novel method to simplify decision functions of support vector machines (SVMs) is proposed in this paper. In our method, a decision function is determined first in a usual way by using all training samples. Next those support vectors which contribute less to the decision function are excluded from the training samples. Finally a new decision function is obtained by using the remaining samples. Experimental results show that the proposed method can effectively simplify decision functions of SVMs without reducing the generalization capability.