A GENERIC ENSEMBLE APPROACH TO ESTIMATE MULTIDIMENSIONAL LIKELIHOOD IN BAYESIAN CLASSIFIER LEARNING

摘要

In Bayesian classifier learning, estimating the joint probability distribution p(x, y)or the likelihood p(x vertical bar y) directly from training data is considered to be difficult, especially in large multidimensional data sets. To circumvent this difficulty, existing Bayesian classifiers such as Naive Bayes, BayesNet, and A eta DE have focused on estimating simplified surrogates of p(x, y) from different forms of one-dimensional likelihoods.