.png){kind=icon}
返回
Large data sets classification using convex-concave hull and support vector machine
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-012-0954-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Normal support vector machine (SVM) is not suitable for classification of large data sets because of high training complexity. Convex hull can simplify the SVM training. However, the classification accuracy becomes lower when there exist inseparable points. This paper introduces a novel method for SVM classification, called convex-concave hull SVM (CCH-SVM). After grid processing, the convex hull is used to find extreme points. Then, we use Jarvis march method to determine the concave (non-convex) hull for the inseparable points. Finally, the vertices of the convex-concave hull are applied for SVM training. The proposed CCH-SVM classifier has distinctive advantages on dealing with large data sets. We apply the proposed method on several benchmark problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers. Compared with the other convex hull SVM methods, the classification accuracy is higher.
Keyword:
ALGORITHM
POINTS
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Support vector machine classification for large data sets via minimum enclosing ball clustering基于最小包围球聚类的大数据集支持向量机分类.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容