It has been established that active learning is effective for learning complex, subjective query concepts for image retrieval. However, active learning has been applied in a conc...
The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the...
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm...
Abstract. Active Shape Models are commonly used to recognize and locate different aspects of known rigid objects. However, they require an off-line learning stage, such that the ex...
Michael Fussenegger, Peter M. Roth, Horst Bischof,...