By Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghasem Miremadi, Shaahin Hessabi
This publication constitutes the revised chosen papers of the thirteenth overseas CSI laptop convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four general papers awarded including sixty eight poster displays have been conscientiously reviewed and chosen from a complete of 426 submissions.
The papers are geared up in topical sections on learning/soft computing, set of rules idea, SoC and NoC, wireless/sensor networks, video processing and similar issues, processor structure, AI/robotics/control, scientific snapshot processing, p2p/cluster/grid structures, cellular advert hoc networks, net, sign processing/speech processing, misc, safeguard, photograph processing purposes 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
K2 is the best learning algorithm according to time complexity but almost the worst one in accuracy compared to other algorithms. R. M. B. Menhaj show that Bayesian network with structural learning outperforms the Naive Bayes classifier. Based on the results obtained for classification of 5 different datasets, we believe that the BN classifiers can be used more often in many applications. As future works, we want to investigate the use of evolutionary based algorithms for searching the state space in structural learning.
There are different ways of establishing the Bayesian network structure [9, 4]. Learning structure means learning conditional independencies from observations. The parameters of a BN with a given structure are estimated by ML or MAP estimation. One can use a similar method for choosing a structure for the BN that fits to the observed data. However, structural learning is slightly different than parameter learning. If we consider ML estimation or MAP estimation with uninformative priors then the structure that maximizes the likelihood will be the result.
4) There is a trade-off between precision and recall. Increasing one of them decreases the other one. Hence, we use F-Measure as a general measure for evaluating the efficiency of the algorithm. 3 Selecting the Optimal Number of Patterns As said in section 3-3, we extract 166 lexico-syntactic patterns for finding ‘is’ relations and prioritize them according to their power in showing ‘is’ relation. In order to determine the optimal number of patterns for training the neural network, we train it with different number of patterns.