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Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
[article]
2016
arXiv
pre-print
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to the kernel parameter and the trade-off parameter. Although many extensions of SVC have been developed, to the best of our knowledge, there is still no algorithm that is able to effectively estimate the two crucial parameters in SVC without supervision. In this
arXiv:1608.01198v2
fatcat:c4zytmagrjfrfhxfxb5xwcbcdi