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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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.251-260, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_27〉
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Chenzhe Zhou, Ilia Nouretdinov, Zhiyuan Luo, Alex Gammerman. SVM Venn Machine with k-Means Clustering. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.251-260, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_27〉. 〈hal-01391052〉

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