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SVM Venn Machine with k-Means Clustering

Abstract : 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.
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Chenzhe Zhou, Ilia Nouretdinov, Zhiyuan Luo, Alex Gammerman. SVM Venn Machine with k-Means Clustering. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.251-260, ⟨10.1007/978-3-662-44722-2_27⟩. ⟨hal-01391052⟩

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