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Titlebook: Strengthening Links Between Data Analysis and Soft Computing; Przemyslaw Grzegorzewski,Marek Gagolewski,María Án Book 2015 Springer Intern

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On Incomplete Label Ranking with IF-setstive in the learning step and therefore the applications of the Mallows model in practical label ranking problems or in recommender systems are limited. In this paper, we show how to improve the Mallows model using IF-sets so it may become more simple and more effective for analyzing vague preferenc
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Forecasting Short Time Series with the Bayesian Autoregression and the Soft Computing Prior Informatseries data. We introduce an innovative approach to discover prior information from experts in their fields and incorporate this into the bayesian autoregressive forecasting. It is observed that for the short time series, the bayesian method combined with the soft computing techniques, especially th
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Learning Structure of Bayesian Networks by Using Possibilistic Upper Entropydge is available over the structure, score functions based on information theory are used to balance the entropy of the conditional probability tables with network complexity. Clearly, this complexity has a high impact on the uncertainty about the estimation of the conditional distributions. However
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On Fuzzy Equations with 2 ×2 Matricesred. We derive that the probability of solving such a system is very low, namely . for Gödel norm, . for Łukasiewicz norm, . for product norm and zero for drastic norm. These results are surprising compared to the case of a finite vector space, where the probability is one.
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Empirical Sensitivity Analysis on the Influence of the Shape of Fuzzy Data on the Estimation of Somelusions of such an analysis. More concretely, samples of fuzzy data are simulated in accordance with different assumptions (distributions) concerning four relevant points (namely, those determining their core and support), and later, by preserving core and support, the ‘arms’ are changed by consider
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On the Robustness of Absolute Deviations with Fuzzy Dataample mean or the sample variance, can be substantially affected by these observations, which are referred to as outliers. Robust statistics provides methods which are not unduly influenced by atypical data..In this paper an introductory empirical study is developed to compare the robustness of the
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