THE EFFECT OF SCALE TRANSFORMATION FOR HYPER SURFACE CLASSIFICATION METHOD
Hyper Surface Classification (HSC), which is based on Jordan Curve Theorem in Topology, has been proven to be a simple and effective method for classifying a large database in our previous work. In this paper, through theoretical analysis, we find that different scales may affect the training process of HSC, which influences its classification performance. To investigate the impact and find a suitable scale, the scale transformation of HSC is studied. The experimental results show that the accuracy increases with the shrinkage of the scale, but the effect is tiny. Furthermore, we find that some samples become inconsistent and repetitious when the scale is adequately small, because of the powerlessly providing enough precision by the data type of computer. Fortunately, HSC can get a high performance with common scales as experiments exhibit.
Hyper Surface Classification HSC Scale Transformation
QING HE XU-DONG MA FU-ZHEN ZHUANG ZHONG-ZHI SHI
The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese
国际会议
2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)
保定
英文
1856-1860
2009-07-12（万方平台首次上网日期，不代表论文的发表时间）