Read e-book online Advances in Computer Science and Engineering: 13th PDF

By Mohammad Taheri, Elham Chitsaz, Seraj D. Katebi, Mansoor Z. Jahromi (auth.), Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghassem Miremadi, Shaahin Hessabi (eds.)

ISBN-10: 3540899847

ISBN-13: 9783540899846

ISBN-10: 3540899855

ISBN-13: 9783540899853

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

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

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Read or Download Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers PDF

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Extra 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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The structure of the naive Bayes network 4 Experimental Results We run our experiments on the 5 datasets listed in Table 1. Table 1. Experimental datasets Dataset Number of instances Number of attributes Number of classes Lens D1 24 5 3 iris labor D2 D3 150 57 4 17 3 3 segment D4 1500 20 7 soybean D5 683 36 19 The contact lens data (dataset D1) tells us the kind of contact lens to prescribe, given certain information about a patient. The iris dataset (D2), is arguably the most famous dataset used in data mining, contains 150 examples each of three types of plant: Iris setosa, Iris versicolor, and Iris virginica.

Fatemi In order to solve the classification problem, after extracting and ranking the patterns, we find an optimal combination of co-occurrence in the web and lexico-syntactic patterns factors using a neural network model. To learn suitable patterns we use WordNet for extracting positive samples (words with ‘is-a’ relations) and negative ones are constructed by random combination of words. 2 Co-Occurrence Measure For computing the co-occurrence rate of two words, such as and , we search ‘ ’ query using Google search engine.

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. References 1. : Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, LosAlios (1988) 2.

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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 Mohammad Taheri, Elham Chitsaz, Seraj D. Katebi, Mansoor Z. Jahromi (auth.), Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghassem Miremadi, Shaahin Hessabi (eds.)


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