A Novel Online Bayes Classifier

Abstract

We present VIGO, a novel online Bayesian classifier for both binary or multiclass problems. In our model, variational inference for multivariate Gaussian distribution technique is exploited to approximate the class conditional probability density functions of data in an online manner. Besides being a conservative learner with a low number of updates compared with many other popular algorithms, VIGO algorithm can be updated in a minibatch of an arbitrary size which makes it robust with noise data. Experiments over a large number of UCI datasets demonstrate the advantage of VIGO with many state-of-the-art methods presented in LIBOL - a prevalent library for online learning algorithms.