Novel multiclass classifiers based on the minimization of the within-class variance
IEEE Transactions on Neural Networks
Kernel-matching pursuits with arbitrary loss functions
IEEE Transactions on Neural Networks
Multi-agent system for customer relationship management with SVMs tool
International Journal of Intelligent Information and Database Systems
Multicategory nets of single-layer perceptrons: complexity and sample-size issues
IEEE Transactions on Neural Networks
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Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the multiclass case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.