Many ranking models have been proposed in information retrieval, and recently machine learning techniques have also been applied to ranking model construction. Most of the existin...
Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, H...
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
The paper addresses methods for parameter sensitivity analysis in a large, nonlinear, mechanistic model which is to be run in an online estimation scheme. The parameter sensitivit...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...