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Multiclass Data Classification Using Multinomial Logistic Gaussian Process Model

  • Wanhyun Cho
  • Soonyoung Park
  • Sangkyoon Kim
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

We propose the multiclass data classification method using Bayesian logistic Gaussian process model. First, we have defined the multinomial logistic Gaussian process classification model. Second, we have derived the predictive distribution of the classification variable corresponding to the new input data point by using a variational Bayesian inference method. Finally, in order to verify the performance of the proposed model, we conducted experiments using Iris real dataset. From the experimental results, we can see that the proposed model has achieved superior classification ability.

Keywords

Multiclass data classification Multinomial logistic Gaussian process model Variational Bayesian inference Predictive distribution Iris real data 

Notes

Acknowledgments

This work was jointly supported by the Korea Research Foundation (NRF-2017R1D1A1B03028808).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of StatisticsChonnam National UniversityGwangjuSouth Korea
  2. 2.Department of Electronics EngineeringMokpo National UniversityMuanSouth Korea

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