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Parametric non-parallel support vector machines for pattern classification
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DOI：10.1007/s10994-022-06238-0.png)
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Abstract
En 中文
This paper proposes Parametric non-parallel support vector machines for binary pattern classification. Through an intelligent redesigning of the Support vector machine optimisation, not only do we bring noise resilience into the model, but also retain its sparsity. Our model exhibits properties similar to Support vector machines, hence many SVM related learning algorithms can be extended to make it scalable for large scale problems. Experimental results on several benchmark UCI datasets validate our claims.
Keywords:
Support vector machines
Twin support vector machines
Pinball loss
Noise insensitivity
Sparsity