Ana M. Breda

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The classification of f-tilings was inspired in Stewart Robertson’s work [5], “Isometric Foldings of Riemannian Manifolds” and was initiated by Ana Breda [3], where a complete classification of all monohedral f-tilings of the Riemannian sphere S was done. Here we shall classify, up to a spherical isometry, the class of all dihedral f-tilings of S whose(More)
In this paper we present a methodology for describing adaptive educational-game environments and a model that supports the environment design process. These environments combine the advantages of educational games with those derived from the adaptation. The proposed methodology allows the specification of educational methods that can be used for the game(More)
We present Adaptive Bayes, an adaptive incremental version of Naïve Bayes, to model a prediction task based on learning styles in the context of an Adaptive Hypermedia Educational System. Since the student’s preferences can change over time, this task is related with a problem known as concept drift in the machine learning community. For this class of(More)
An isometric folding is a non-expansive locally isometry that sends piecewise geodesic segments into piecewise geodesic segments of the same length. An isometric folding is a continuous map that need not to be differentiable. The points where it is not differentiable are called singular points. The foundations of isometric foldings of Riemannian manifolds(More)
A spherical folding tiling, or f-tiling for short, is an edge-to-edge decomposition of the sphere by geodesic polygons, such that all vertices are of even valency and the sums of alternating angles around each vertex are π. A f-tiling τ is said dihedral if every tile of τ is congruent to either two fixed sets T and Q. In this case T and Q are the prototiles(More)
This thesis mainly addresses the development of adaptive learning algorithms for Bayesian network classifiers (BNCs) in an on-line leaning scenario. In this scenario data arrives at the learning system sequentially. The actual predictive model must first make a prediction and then update the current model with new data. This scenario corresponds to the(More)