Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653398 | Neurocomputing | 2005 | 14 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Xi-Zhao Wang, Qiang He, De-Gang Chen, Daniel Yeung,