We propose a novel scheme for using supervised learning for function-based classification of objects in 3D images. During the learning process, a generic multi-level hierarchical ...
The task of computing molecular structure from combinations of experimental and theoretical constraints is expensive because of the large number of estimated parameters (the 3D co...
Cheng Che Chen, Jaswinder Pal Singh, Russ B. Altma...
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts ...
Abstract. We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment ...
In this paper, we propose a conceptual framework for developing a family of models for Group-Centric information sharing. The traditional approach to information sharing, characte...
Ram Krishnan, Ravi S. Sandhu, Jianwei Niu, William...