Titlebook: Advances in Artificial Intelligence - IBERAMIA 2008; 11th Ibero-American Hector Geffner,Rui Prada,Nuno David Conference proceedings 2008 S

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,Macy-Gy?rgy Prize Lecture: My Milky Way,inuous and some others are discrete. The goal is to compute the posterior distribution of the response variable given the observations, and then use that distribution to give a prediction. The involved distributions are represented as Mixtures of Truncated Exponentials. We test the performance of th

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Forms of Capital and Parental Involvement,background model consists of a competitive neural network based on dipoles, which is used to classify the pixels as background or foreground. Using this kind of neural networks permits an easy hardware implementation to achieve a real time processing with good results. The dipolar representation is

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https://doi.org/10.1007/978-3-319-56442-5ements belonging to other subsets. This problem can be understood as an optimization problem that looks for the best configuration of the clusters among all possible configurations. K-means is the most popular approximate algorithm applied to the clustering problem, but it is very sensitive to the s

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https://doi.org/10.1007/978-3-319-56442-5ep in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes the database. This is done using a structure called

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