Active Media Technology: 7th International Conference, AMT by Ali Ghorbani (auth.), Ning Zhong, Vic Callaghan, Ali A.

By Ali Ghorbani (auth.), Ning Zhong, Vic Callaghan, Ali A. Ghorbani, Bin Hu (eds.)

This e-book constitutes the refereed court cases of the seventh foreign convention on lively Media know-how, AMT 2011, held in Lanzhou, China, in September 2011. The 30 revised complete papers and six keynote talks have been conscientiously reviewed and chosen for inclusion within the publication. they're grouped in topcial sections on facts mining and trend research in energetic media; energetic human-Web interplay and social media; lively net intelligence functions; lively multi-agent and community structures; in addition to expertise intelligence.

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Active Media Technology: 7th International Conference, AMT 2011, Lanzhou, China, September 7-9, 2011. Proceedings

This e-book constitutes the refereed complaints of the seventh foreign convention on energetic Media expertise, AMT 2011, held in Lanzhou, China, in September 2011. The 30 revised complete papers and six keynote talks have been conscientiously reviewed and chosen for inclusion within the ebook. they're grouped in topcial sections on facts mining and trend research in lively media; energetic human-Web interplay and social media; energetic internet intelligence functions; lively multi-agent and community structures; in addition to know-how intelligence.

Extra resources for Active Media Technology: 7th International Conference, AMT 2011, Lanzhou, China, September 7-9, 2011. Proceedings

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Analysis of Autism Prevalence and Neurotoxins Using Combinatorial Fusion and Association Rule Mining. In: BIBE, pp. C. ): Combining Artificial Neural Nets: Ensemble and Modular MultiNet Systems. Perspectives in Neural Computing. F. Hsu et al. : Combinatorial Fusion for Improving Portfolio Performance. In: Advances in Social Science Research Using R, pp. 95–105. : Analysis of data fusion methods in virtual screening: Theoretical model. : Consensus scoring for improving enrichment in virtual screening.

The number of all possible pairwise classifiers is c*(c-1)/2 that it is O(c^2). Using this method without giving up a considerable accuracy, we decrease its order to O(1). This feature of our proposed method makes it applicable for problems with a large number of classes. The experiments show the effectiveness of this method. Also we reached to very good results in Persian handwritten digit recognition which is a very large dataset. References 1. : Bagging Predictors. Journal of Machine Learning 24(2), 123–140 (1996) 2.

It means that although the more diverse classifiers, the better ensemble, it is provided that the classifiers are better than random. An Artificial Neural Network (ANN) is a model which is to be configured to be able to produce the desired set of outputs, given an arbitrary set of inputs. An ANN generally composed of two basic elements: (a) neurons and (b) connections. Indeed each ANN is a set of neurons with some connections between them. From another perspective an ANN contains two distinct views: (a) topology and (b) learning.

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