Modern real-time embedded systems are complex, distributed, feature-rich applications. Model-based development of real-time embedded systems promises to simplify and accelerate the...
Madhukar Anand, Sebastian Fischmeister, Jesung Kim...
This paper presents a statistical learning approach to predicting people's bidding behavior in negotiation. Our study consists of multiple 2-player negotiation scenarios wher...
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
An analysis-by-synthesis framework for face recognition with variant pose, illumination and expression (PIE) is proposed in this paper. First, an efficient 2D-to-3D integrated fac...
Yuxiao Hu, Dalong Jiang, Shuicheng Yan, Lei Zhang,...
Abstract We propose in this paper a novel approach to the classification of discrete sequences. This approach builds a model fitting some dynamical features deduced from the learni...