We present a 2D model-based approach to localizing human body in images viewed from arbitrary and unknown angles. The central component is a statistical shape representation of th...
Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
The Hierarchical Mixture of Experts (HME) is a well-known tree-structured model for regression and classification, based on soft probabilistic splits of the input space. In its o...
The protein inference problem represents a major challenge in shotgun proteomics. Here we describe a novel Bayesian approach to address this challenge that incorporates the predict...
Yong Fuga Li, Randy J. Arnold, Yixue Li, Predrag R...