Abstract
The probabilistic classification vector machine (PCVM) is an effective sparse learning approach for binary classification. This paper presents an extension of the PCVM to the multiclass case, which aims to provide reliable probabilistic outputs by taking advantage of the inherent probabilistic nature of the PCVM. The performance of the proposed multiclass PCVM is evaluated on several benchmark datasets. In particular, the probabilistic outputs are used to assess the confidence in the prediction. The experimental results demonstrate that multiclass PCVMs can make more confident correct predictions than probabilistic multiclass support vector machines and multiclass relevance vector machines.
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