We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We prove the existence of an optimal MAPE model and we show the universal consistency of Empirical Risk Minimization based on the MAPE. We also show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression, and we apply this weighting strategy to kernel regression. The behavior of the MAPE kernel regression is illustrated on simulated data

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Topic Maps provide a bridge between the domains of knowledge representation and information management by building a structured semantic network above information resources. Our research at LIP6 aims at visualizing this semantic layer efficiently, which is a critical issue as Topic Maps may contain millions of elements.This paper is divided into two parts. First, we depict briefly basic Topic Maps concepts. Then, we discuss Topic Maps visualisation requirements and we study how existing visualisation techniques may be applied to Topic Maps representation. We conclude by giving a few directions that could lead to the "ultimate" Topic Map visualisation tool.