A Sequential Heteroscedastic Probabilistic Neural Network for Online Classification

Ali Mahmoudi, Reza Moghadam, Kurosh Madani


In this paper, a novel online classification algorithm called sequential heteroscedastic probabilistic neural network (SHPNN) is proposed. This algorithm is based on Probabilistic Neural Networks (PNNs). One of the advantages of the proposed algorithm is that it can increase the number of its hidden node kernels adaptively to match the complexity of the data. The performance of this network is analyzed for a number of standard datasets. The results suggest that the accuracy of this algorithm is on par with other state of the art online classification algorithms while being significantly faster in majority of cases.


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