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Titlebook: Adaptive and Intelligent Systems; Third International Abdelhamid Bouchachia Conference proceedings 2014 Springer International Publishing

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期刊全称Adaptive and Intelligent Systems
期刊简称Third International
影响因子2023Abdelhamid Bouchachia
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Adaptive and Intelligent Systems; Third International  Abdelhamid Bouchachia Conference proceedings 2014 Springer International Publishing
影响因子This book constitutes the proceedings of the International Conference on Adaptive and Intelligent Systems, ICAIS 2014, held in Bournemouth, UK, in September 2014. The 19 full papers included in these proceedings together with the abstracts of 4 invited talks, were carefully reviewed and selected from 32 submissions. The contributions are organized under the following topical sections: advances in feature selection; clustering and classification; adaptive optimization; advances in time series analysis.
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978-3-319-11297-8Springer International Publishing Switzerland 2014
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Unification of the Forces of Nature,tering and feature selection. The resulting statistical model is learned within a variational framework that we have developed. The merits of the developed model are shown via extensive simulations and experiments when applied to the challenging problem of images categorization.
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,Gravitation and Newton’s Laws,[5], this model has only been tested on controlled synthetic data, showing promising results for classification tasks. Its capabilities on proportional data are investigated and leveraged for texture classification. Comparison to HMM with Gaussian mixtures and to nearest-neighbor classifiers is cond
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General Relativity and Cosmology, has indisputably become the method of choice for this crucial task. Nevertheless, the BoVW approach has various flaws. First, the K-means clustering algorithm for visual dictionary creation is based solely on the Euclidean distance. Second, the size of the visual vocabulary is a user-supplied param
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Basic Concepts of Algebraic Topology used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) we
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