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Advances in Computer Science and Engineering: 13th by Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi,

By Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, Shaahin Hessabi

This booklet constitutes the revised chosen papers of the thirteenth foreign CSI laptop convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four normal papers provided including sixty eight poster shows have been conscientiously reviewed and chosen from a complete of 426 submissions.

The papers are equipped in topical sections on learning/soft computing, set of rules concept, SoC and NoC, wireless/sensor networks, video processing and comparable themes, processor structure, AI/robotics/control, clinical photograph processing, p2p/cluster/grid platforms, cellular advert hoc networks, net, sign processing/speech processing, misc, protection, photograph processing functions in addition to VLSI.

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In general, this optimization problem is intractable, yet for certain restricted classes of networks there are efficient algorithms requiring polynomial time (in the number of variables in the network). 1 Scoring Structure The criteria for selecting a structure have two terms. One for maximizing the likelihood and one for minimizing the complexity. The log-likelihood score is a simple score. While log-likelihood score is very simple, is not suitable for learning the structure of the network, since it tends to favor complete graph structures (in which every variable is connected to every other variable).

The proposed algorithm produces higher values of precision in compare with existing approaches that use limited and non-automatic patterns. Training and execution time complexity is not high, because instead of loading the whole page we only load snippets. We construct an optimal set of lexico-analysis patterns by investigating the impact that the number of patterns has on the efficiency of the algorithm. 82 for precision and recall respectively. 34. Finally, we used sensitivity analysis for finding the relative importance of different factors that play role in deducing ‘is’ relations.

Ant-Q is a link between reinforcement learning and Ant Colony Optimization. However, ACS simply and more efficiently describes the same behavior as Ant-Q. Two strategies are used in ACS to increase the previous information exploitation. At first, trails are updated by the best ant, like MMAS, and secondly, ants select the next city, using a pseudo-random proportional rule [11]. The rule states that with probability q the city j is selected, where j arg max τ, . η, , while with the probability 1 q a city is chosen using (1).

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