Multiclass Posterior Probability Support Vector Machines

Mehmet Gönen, Ayşe Gönül Tanuğur, and Ethem Alpaydın

IEEE Transactions on Neural Networks, Vol. 19, No. 1, January 2008

Abstract:

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 multi-class case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark datasets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.

Index Terms:

Density estimation, kernel machines, multiclass classification, support vector machines (SVMs).

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