The paper presents a novel multi-view learning framework based on variational inference. We formulate the framework as a graph representation in form of graph factorization: the g...
We present a computationally efficient, on-line graph structure estimation method for model-based scene interpretation. Different scenes have different hierarchical graphical mode...
Modeling visual concepts using supervised or unsupervised machine learning approaches are becoming increasing important for video semantic indexing, retrieval, and filtering appli...
: This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning meth...
Supervised learning deals with the inference of a distribution over an output or label space Y conditioned on points in an observation space X , given a training dataset D of pair...