We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
In this paper, Multi-View Expectation and Maximization algorithm for finite mixture models is proposed by us to handle realworld learning problems which have natural feature split...
Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has running ...
Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is...
Xiuyao Song, Chris Jermaine, Sanjay Ranka, John Gu...