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Experimentation has an important role in determining the capacities and restrictions of machine learning (ML) systems. In this paper we present the definition of some sensitivity and evaluation criteria which can be used to perform an evaluation of learning systems. Moreover, in order to overcome some of the limitations of real data sets, we introduce the specification of a parametrable generator...
Co-clustering has been defined as a way to organize simultaneously subsets of instances and subsets of features in order to improve the clustering of both of them. In previous work, we proposed an efficient co-similarity measure allowing to simultaneously compute two similarity matrices between objects and features, each built on the basis of the other. Here we propose a generalization of this approach...
Co-clustering has been widely studied in recent years. Exploiting the duality between objects and features efficiently helps in better clustering both objects and features. In contrast with current co-clustering algorithms that focus on directly finding some patterns in the data matrix, in this paper we define a (co-)similarity measure, named X-Sim, which iteratively computes the similarity between...
Many researchers consider interactive learning environments to be interesting solutions for overcoming the limits of classical one-to-many teaching methods. However, these environments should incorporate accurate representations of student knowledge to provide relevant guidance. In a problem-solving environment, one way to build and update this student model is model tracing, or using a detailed representation...
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