TY - JFULL AU - Wanhyun Cho and Soonja Kang and Sangkyoon Kim and Soonyoung Park PY - 2015/1/ TI - Multinomial Dirichlet Gaussian Process Model for Classification of Multidimensional Data T2 - International Journal of Computer and Information Engineering SP - 2445 EP - 2450 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10003081 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 108, 2015 N2 - We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods. ER -