Read e-book online Advances in Knowledge Discovery and Management: Volume 6 PDF

By Fabrice Guillet, Bruno Pinaud, Gilles Venturini

This booklet offers a suite of consultant and novel paintings within the box of information mining, wisdom discovery, clustering and class, in response to elevated and remodeled types of a range of the simplest papers initially awarded in French on the EGC 2014 and EGC 2015 meetings held in Rennes (France) in January 2014 and Luxembourg in January 2015. The booklet is in 3 elements: the 1st 4 chapters speak about optimization concerns in info mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters concentrate on semantics, ontologies and social networks.
Written for PhD and MSc scholars, in addition to researchers operating within the box, it addresses either theoretical and useful facets of data discovery and management.

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Rodríguez, J. J. (2007). Classifier ensembles with a random linear oracle. IEEE Transactions on Knowledge and Data Engineering, 19(4), 500–508. Lange, K. (2004). Optimization. Springer Texts in Statistics. New York: Springer. , & Thompson, K. (1992). An analysis of bayesian classifiers. In National Conference on Artificial Intelligence (pp. 223–228). Nesterov, Y. (2004). Introductory lectures on convex optimization: A basic course. Applied optimization. Boston: Kluwer Academic Publishers. Nesterov, Y.

Dd } be a set of d dimensions. Let us denote by dom(Di ) the domain associated with dimension Di . Let S be a subset of dom(D1 ) × . . × dom(Dd ), p and q two points of S , and i a preference relation on Di . One says that p dominates q on D ( p is better than q according to Pareto order), denoted by p D q, iff ∀i ∈ [1, d] : pi i qi and ∃ j ∈ [1, d] : p j j q j A skyline query on D applied to a set of points S , whose result is denoted by SkyD (S ), according to preference relations i , produces the set of points that are not dominated by any other point of S : SkyD (S ) = { p ∈ S | q ∈ S : q D p} Depending on the context, one may try, for instance, to maximize or minimize the values of dom(Di ), assuming that dom(Di ) is a numerical domain.

According to the values of L and N , it can be necessary to use the instances in several mini-batches. In this case, the datasets are randomly shuffled between two mini-batches. In the results, ‘SNB’ stands for the performance of a selective naïve Bayes classifier with model averaging (Boullé 2007a). 1 Experiments on Optimization Quality First of all, we have studied the PGDMB algorithm performance, that is to say, the performance of the projected gradient descent algorithm according to the mini-batch size denoted L, without using MS or VNS metaheuristic.

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