Language modeling is an effective and theoretically attractive probabilistic framework for text information retrieval. The basic idea of this approach is to estimate a language mo...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian m...
We propose a competitive finite mixture of neurons (or perceptrons) for solving binary classification problems. Our classifier includes a prior for the weights between different n...
Karthik Sridharan, Matthew J. Beal, Venu Govindara...
Abstract. The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid commonly faced numerical difficulties encountered with EM. Its princip...
In many practical applications, the data is organized along a manifold of lower dimension than the dimension of the embedding space. This additional information can be used when le...