By S. Bassis and C.F. Morabito B. Apolloni
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Additional resources for Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri Sul Mare, Salerno, Italy May 28-30 2009, Volume 204 Frontiers in Artificial ... Intelligent Engineering Systems)
There is a long history related to this kind of approach, also because of the excitement sparked by the similarities with the working of the nervous system (Bayesian reasoning). g. FPGA). Factor Graphs (FG) , when applied to the probabilistic framework, are a different graphical form for describing Bayesian networks . The network of random variables in a factor graph is modeled as the product of functions: the branches represent the variables and the nodes the functions. The FG approach has shown greater engineering appeal compared to the Bayesian network counterpart, because it maps the system directly to a more familiar block diagram.
Collaboration at the basis of sharing focused information: the opportunistic networks. , 2009. In press.  B. Apolloni, S. Bassis, and S. Gaito. Fitting opportunistic networks data with a Pareto distribution. In B. Apolloni, R. J. Howlett, and L. C. Jain, editors, Join Conference KES 2007 and WIRN2009, volume 4694 of LNAI, pages 812–820, Vietri sul Mare, Salerno, 2007. Springer-Verlag.  B. Apolloni, S. Bassis, S. Gaito, and D. Malchiodi. Appreciation of medical treatments by learning underlying functions with good conﬁdence.
S. In particular, we marked the points corresponding to the medians of these sets, assumed to be their representatives. f. with this median curve and go back to the m-parameters of the underlying model by back-regressing from the median s-parameters to the mobility ones, we obtain: on the one hand, values in line with the overall trend of both a and b with α and λ, as modeled in the previous sections. 5) discussed in the previous section. With c things go worse. But this is to be expected given both the tuning role of this parameter and the indirect inﬂuence of m-parameters on it.