Advances in Knowledge Discovery and Management: Volume 6 by Fabrice Guillet, Bruno Pinaud, Gilles Venturini

By Fabrice Guillet, Bruno Pinaud, Gilles Venturini

This booklet provides a set of consultant and novel paintings within the box of information mining, wisdom discovery, clustering and class, according to improved and remodeled types of a range of the simplest papers initially provided in French on the EGC 2014 and EGC 2015 meetings held in Rennes (France) in January 2014 and Luxembourg in January 2015. The e-book is in 3 components: the 1st 4 chapters speak about optimization issues in information mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters specialise in semantics, ontologies and social networks.
Written for PhD and MSc scholars, in addition to researchers operating within the box, it addresses either theoretical and sensible elements of data discovery and management.

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Such databases open new challenges for assessing the robustness and generalization capabilities of gesture recognition algorithms on diversified motion data sets. Besides the quality of recognition, the complexity of the algorithms and their computational cost is indeed a major issue, especially in the context of real-time interaction. We address in this paper the recognition of isolated gestures from motion captured data.

In International Joint Conference on Neural Network Proceedings (pp. 1680–1688). Boullé, M. (2007a). Compression-based averaging of selective naive bayes classifiers. Journal of Machine Learning Research, 8, 1659–1685. Boullé, M. (2007b). Recherche d’une représentation des données efficace pour la fouille des grandes bases de données. PhD thesis, Ecole Nationale Supérieure des Télécommunications. , & Xiao, L. (2012). Optimal distributed online prediction using mini-batches. Journal of Machine Learning Research, 13(1), 165–202.

To 3, 4, 5. For a VNS metaheuristic, the train compression rate is significantly improved for resp. 18, 19, 23 of the 36 datasets with an optimization level equal resp. to 3, 4, 5. The VNS metaheuristic seems then better than the MS metaheuristic: the guided exploration within a variable sized neighborhood from the best minimum encountered enables a more fruitful exploration than a purely random exploration. Figure 3 illustrates this iterations “waste” phenomenon with multi-start at the beginning of each start.

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