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In the domain of inductive learning from examples, usually, training data are not evenly distributed in the input space. This makes global and eager methods, like Neural Networks, not very accurate in those cases. On the other hand, lazy methods have the problem of how to select the best examples...
In this paper we present a model-based approach to the on-line diagnosis of dynamic systems. We model the system to be diagnosed as a discrete, synchronous transition system and capture temporal phenomena such as the change of the system inputs, the evolution of the system internal status and the...
Massive data sets have become common in many applications making the task of finding an optimum subset of attributes extremely difficult. Traditional feature selection techniques can be very inefficient in high dimensional data, especially when the subset evaluation is obtained through a learning...