使用减少图形的线性组合构建多个分类器系统
Anthony Gillioz1, Kaspar Riesen1,2
1Institute of Computer Science, University of Bern, Neubrückstrasse 10, 3012 Bern, Switzerland.
概括
本研究引入了一种新的图形分类框架,使用缩小的图形子空间和节点中心性措施. 结合这些子空间的距离可以提高一般图形的分类准确性.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 由于复杂的结构,一般图形分类具有挑战性.
- 标准的模式识别方法在图形数据上经常失败.
- 像图形匹配和内核机器这样的现有方法都有局限性.
研究的目的:
- 为一般图形分类提出一个新的框架.
- 为了提高精度,利用缩小图形子空间的信息.
- 为了解决当前图形分类技术的局限性.
主要方法:
- 使用节点中心性指标生成缩小图.
- 计算图形编辑子空间内的距离.
- 结合使用线性组合进行分类的距离.
主要成果:
- 拟议的框架有效地对一般图形进行了分类.
- 使用多个缩小的图形子空间可以提高分类性能.
- 在六个数据集上的实证验证证了该系统的好处.
结论:
- 新的框架为一般图形分类提供了一个有希望的方法.
- 将图形子空间中的信息结合起来是有益的.
- 该方法表现出比现有技术更好的准确性.
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