Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Wednesday, November 2, 2016 - 5:18:07 PM
Last modification on : Thursday, March 5, 2020 - 5:41:12 PM
Long-term archiving on: : Friday, February 3, 2017 - 3:19:40 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



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⟩



Record views


Files downloads