SVM Venn machine with k-means clustering

被引：0
作者：
Zhou, Chenzhe [1 ]
Nouretdinov, Ilia [1 ]
Luo, Zhiyuan [1 ]
Gammerman, Alex [1 ]
机构：
[1] Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey
来源：
IFIP Advances in Information and Communication Technology | 2014年 / 437卷
关键词：
k-means clustering; Support vector machine; Venn machine;
D O I：
10.1007/978-3-662-44722-2_27
中图分类号：
学科分类号：
摘要：
In this paper, we introduce a new method of designing Venn Machine taxonomy based on Support Vector Machines and k-means clustering for both binary and multi-class problems. We compare this algorithm to some other multi-probabilistic predictors including SVM Venn Machine with homogeneous intervals and a recently developed algorithm called Venn-ABERS predictor. These algorithms were tested on a range of real-world data sets. Experimental results are presented and discussed. © IFIP International Federation for Information Processing 2014.
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页码：251 / 260
页数：9
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