Agents and Data Mining Interaction: 10th International - download pdf or read online

By Longbing Cao

ISBN-10: 3319202294

ISBN-13: 9783319202297

ISBN-10: 3319202308

ISBN-13: 9783319202303

This e-book constitutes the completely refereed and revised chosen papers from the tenth overseas Workshop on brokers and information Mining Interactions, ADMI 2014, held in Paris, France, in may perhaps 2014 as satellite tv for pc workshop of AAMAS 2014, the thirteenth foreign convention on self sustaining brokers and Multiagent Systems.

The eleven papers provided have been conscientiously reviewed and chosen from quite a few submissions for inclusion during this quantity. They current present learn and engineering effects, in addition to strength demanding situations and clients encountered within the respective groups and the coupling among brokers and information mining.

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Extra resources for Agents and Data Mining Interaction: 10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers

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24(3), 64–72 (2009) 5. : Ensemble methods in machine learning. , Roli, F. ) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000) 6. : Methods and algorithms of collective recognition. Autom. Remote Control 69(11), 1821–1851 (2008) 7. : Contextdriven data and information fusion. In: Proceedings of International Conference on Information Fusion (Fusion 2012), Singapore, 9-12 July 2012 8. : Toward ontology-based personalization of a recommender system in social network. Int. J. Comput.

These properties depict the differences with synchronous diffusion process in multiplex networks and reveal influences of the two aspects of temporal dynamics on propagation process. 1 Numerical Modeling The main aim of this subsection is calculating the fraction of expected active agents in networks in the case of a certain activation probability and one initialized active agent [10]. To reduce the complexity of networks, many numerical models rely on the mean-field assumption which means the uniformly distribution of active agents in topological networks [9,19].

4, it can be found that the predicted propagation processes of numerical modeling can fit well with the multi-agent simulations. Meanwhile, numerical modeling can grasp the macroscopic features of the two aspects of temporal dynamics. Before the stationary state is reached, low cross-layers probability can lead to the diverse scales of propagations in couple layers as shown in Figure (a). However, fractions of active agents in coupled layers are coincided with each Fig. 4. Comparison between numerical modeling and multi-agent simulations 34 Z.

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Agents and Data Mining Interaction: 10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers by Longbing Cao


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