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

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

ISBN-10: 3540899847

ISBN-13: 9783540899846

This e-book constitutes the revised chosen papers of the thirteenth overseas CSI machine convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four average papers awarded including sixty eight poster displays have been conscientiously reviewed and chosen from a complete of 426 submissions.

The papers are prepared in topical sections on learning/soft computing, set of rules conception, SoC and NoC, wireless/sensor networks, video processing and similar subject matters, processor structure, AI/robotics/control, scientific photo processing, p2p/cluster/grid structures, cellular advert hoc networks, internet, sign processing/speech processing, misc, safeguard, photo processing functions in addition to VLSI.

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Additional info for Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers

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Our proposed algorithm is based on lexico-syntactic patterns and, on the other hand, it uses co-occurrences of words for extracting ‘is’ relations. These two measures are 24 M. Neshati, H. Abolhassani, and H. Fatemi combined by neural network model to create a powerful classifier for deducing ‘is’ relations. We use a training set, which is produced automatically from WordNet, for training the neural network. The proposed algorithm produces higher values of precision in compare with existing approaches that use limited and non-automatic patterns.

It is obvious that increasing the number of patterns will cause weaker patterns to be added to the neural network model, therefore decreasing the precision of the algorithm. In contrast with precision, recall has an increasing behavior because increasing the number of patterns will cause more ‘is’ relations to be extracted and therefore increase the recall. 4 Precision Diagram vs. Number of Snippets We are not able to investigate all retrieved snippets; instead we only investigate those with top rank for extracting patterns.

Finding the optimal structure of a BN from data has been shown to be NP-hard [3]. Thus, heuristic and approximate learning algorithms are the realistic solution. Therefore, greedy score-based algorithms [3] have been developed to provide more efficient structure learning at an accuracy tradeoff. This paper aims to propose the use of structural learning in order to determine structure of the BN classifier. K2, Hill Climbing, Iterative Hill climbing and simulated annealing have been developed to provide more efficient structure learning through more investigation on MDL, BIC and AIC scores.

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Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers by Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, Shaahin Hessabi


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