CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.07 - July
Issue No.07 - July (2010 vol.32)
Sarunas Raudys , Vilnius University, Vilnius
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.72
A novel loss function to train a net of K single-layer perceptrons (KSLPs) is suggested, where pairwise misclassification cost matrix can be incorporated directly. The complexity of the network remains the same; a gradient's computation of the loss function does not necessitate additional calculations. Minimization of the loss requires a smaller number of training epochs. Efficacy of cost-sensitive methods depends on the cost matrix, the overlap of the pattern classes, and sample sizes. Experiments with real-world pattern recognition (PR) tasks show that employment of novel loss function usually outperforms three benchmark methods.
Cost-sensitive learning, loss function, pairwise classification, perceptron.
Sarunas Raudys, "Pairwise Costs in Multiclass Perceptrons", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 7, pp. 1324-1328, July 2010, doi:10.1109/TPAMI.2010.72