Multiclass Data Classification Using Multinomial Logistic Gaussian Process Model
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.
KeywordsMulticlass data classification Multinomial logistic Gaussian process model Variational Bayesian inference Predictive distribution Iris real data
This work was jointly supported by the Korea Research Foundation (NRF-2017R1D1A1B03028808).
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