A genetic algorithm for solving the inverse problem of support vector machines
A genetic algorithm for solving the inverse problem of support vector machines
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DOI：
10.1016/j.neucom.2005.05.006
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发表时间：
2005-10
期刊：
影响因子：
6
通讯作者：
Xizhao Wang;Qiang He;De-gang Chen;D. Yeung
中科院分区：
文献类型：
--
作者：
Xizhao Wang;Qiang He;De-gang Chen;D. Yeung
研究了支持向量机的一个逆问题。逆问题是如何将给定的数据集分割成两个簇，从而使两个簇之间的差值达到最大。这里，根据支持向量生成的分离超平面来定义边界。这个问题很难给出一个确切的解决方案。在本文中，我们设计了一个遗传算法来解决这个问题。数值仿真结果表明了该算法的可行性和有效性。针对支持向量机反问题的研究，设计了一种生成泛化能力较强的决策树的启发式算法。
This paper investigates an inverse problem of support vector machines (SVMs). The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains the maximum. Here the margin is defined according to the separating hyper-plane generated by support vectors. It is difficult to give an exact solution to this problem. In this paper, we design a genetic algorithm to solve this problem. Numerical simulations show the feasibility and effectiveness of this algorithm. This study on the inverse problem of SVMs is motivated by designing a heuristic algorithm for generating decision trees with high generalization capability.