Multiclass Posterior Probability Support Vector Machines


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… (More)
DOI: 10.1109/TNN.2007.903157


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@article{Gnen2008MulticlassPP, title={Multiclass Posterior Probability Support Vector Machines}, author={Mehmet G{\"{o}nen and Ayse G{\"{o}n{\"{u}l Tanugur and Ethem Alpaydin}, journal={IEEE Transactions on Neural Networks}, year={2008}, volume={19}, pages={130-139} }