Multiclass Posterior Probability Support Vector Machines

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… (More)
DOI: 10.1109/TNN.2007.903157

Topics

11 Figures and Tables

Statistics

010203020082009201020112012201320142015201620172018
Citations per Year

97 Citations

Semantic Scholar estimates that this publication has 97 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@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} }