Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
Learning communities from a graph is an important problem in many domains. Different types of communities can be generalized as link-pattern based communities. In this paper, we p...
Bo Long, Xiaoyun Xu, Zhongfei (Mark) Zhang, Philip...
In this paper we extend the maximum spanning tree (MST) dependency parsing framework of McDonald et al. (2005c) to incorporate higher-order feature representations and allow depen...
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...