We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs ...
Katherine A. Heller, Sinead Williamson, Zoubin Gha...
In this paper, we propose a Bayesian network framework for explicitly modeling components and their relationships of Korean Hangul characters. A Hangul character is modeled with h...
In this paper, we present a Gaussian mixture model based approach to capture the spatial characteristics of any target signal in a sensor network, and further propose a temporally...
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
Information filtering has made considerable progress in recent years.The predominant approaches are content-based methods and collaborative methods. Researchers have largely conc...