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Bayes-N: An Algorithm for Learning Bayesian Networks from Data Using Local Measures of Information Gain Applied to Classification Problems
Bayes-N is an algorithm for Bayesian network learning from data based on local measures of information gain, applied to problems in which there is a given dependent or class variable and a set of independent variables from which we want to predict the class variable on new cases. Expand
A Method Based on Genetic Algorithms and Fuzzy Logic to Induce Bayesian Networks
A method to induce bayesian networks from data to overcome some limitations of other learning algorithms is proposed. Expand
Characterization of the Cuban biopharmaceutical industry from collaborative networks
Studies of scientific collaboration have introduced the concepts of collaborative networks. These networks may represent the social structure of a community of researchers or knowledge transmissionExpand
Self-Organizing Maps with Cokriging Porosity Applied to Sand-Shale Reservoirs using Seismic
We selected seismograms from a 3D seismic survey acquired at the Waggoner Ranch, Texas to characterize the distribution of porosities in each lithofacie. Porosity images were obtained using cokrigingExpand
Regularization methods vs large training sets
In this paper, we make an intercomparison of the advantage of regularization methods vs large training datasets when used as methods to reduce the overtraining and overfitting effects when training an ANN. Expand