In this paper, we introduce a new method of designing Venn Machine taxonomy based on Support Vector Machines and k-means clus- tering for both binary and multi-class problems. We compare this algo- rithm 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.
['Chenzhe Zhou', 'Ilia Nouretdinov', 'Zhiyuan Luo', 'Alexander Gammerman']
SVM Venn Machine with k-Means Clustering