Spatial priors play crucial roles in many high-level vision tasks, e.g. scene understanding. Usually, learning spatial priors relies on training a structured output model. In this...
This paper develops algorithms to train support vector machines when training data are distributed across different nodes, and their communication to a centralized processing unit...
Pedro A. Forero, Alfonso Cano, Georgios B. Giannak...
In recent years the Markov Random Field (MRF) has
become the de facto probabilistic model for low-level vision
applications. However, in a maximum a posteriori
(MAP) framework, ...
Oliver J. Woodford, Carsten Rother, Vladimir Kolmo...
The max-cut and stable set problems are two fundamental NP-hard problems in combinatorial optimization. It has been known for a long time that any instance of the stable set probl...
This work shows asymptotic convergence to global optima for a family of dynamically scaled genetic programming systems where the underlying population consists of a fixed number o...