Papers by Carlos Quintero
Graph-based manifold learning techniques have become of paramount importance when researchers hav... more Graph-based manifold learning techniques have become of paramount importance when researchers have been faced to nonlinear data. These techniques have allowed them to discover relations that usual approaches such as PCA and MDS were incapable of. However, properties such as nonuniform sampling, varied topological substructures and highly curved manifolds still represent a challenge to these methods. We propose a graph building framework that strives at capturing the topological structures hidden in the data by means of a locality linear characterization combined with a MST-based noise model. We propose two algorithms under such framework that show improved performance over usual approaches.
In this paper we describe the design and implementation of an educational methodology based on a ... more In this paper we describe the design and implementation of an educational methodology based on a robotic platform used for the small size league (SSL) challenge of the RoboCup initiative. The methodology is based on three main aspects of the learning process, namely classical conditioning, reinforcement learning and cognitive learning. This is achieved through the combination of robotic concepts applied to the soccer problem; a highly interesting topic to the students, together with skill oriented modules. We show practical results achieved after applying this methodology in specific courses in an undergraduate electrical engineering program. Our initial results demonstrate that it is possible to attain significantly better results in terms of learnt concepts and motivation when using our robotic soccer based strategy.
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Papers by Carlos Quintero