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Multi-Kernel Support Vector Clustering For Multi-Class Classification by dridabs is a document available to read on EtoBox.

What is Multi-Kernel Support Vector Clustering For Multi-Class Classification about?

The document presents a two-stage multi-kernel learning algorithm for multi-class classification using support vector clustering (SVC). It addresses the challenges of hyperparameter selection in SVC by constructing multiple SVCs for different classes and integrating their outputs through a discriminant function. Experimental results demonstrate that this approach outperforms existing methods on various datasets.

Author

dridabs

Language

EN