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130 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 1, JANUARY 2008 Multiclass Posterior Probability
 

Summary: 130 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 1, JANUARY 2008
Multiclass Posterior Probability
Support Vector Machines
Mehmet Gönen, Ays¸e Gönül Tanugur, and Ethem Alpaydin, Senior Member, IEEE
Abstract--Tao et al. have recently proposed the posterior proba-
bility support vector machine (PPSVM) which uses soft labels de-
rived 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 bias­variance 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 canon-
ical SVM using significantly fewer support vectors.
Index Terms--Density estimation, kernel machines, multiclass
classification, support vector machines (SVMs).
I. INTRODUCTION
SUPPORT VECTOR MACHINE (SVM) is the optimal
margin linear discriminant trained from a sample of

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences