By Mohammad Taheri, Elham Chitsaz, Seraj D. Katebi, Mansoor Z. Jahromi (auth.), Hamid Sarbazi-Azad, Behrooz Parhami, Seyed-Ghassem Miremadi, Shaahin Hessabi (eds.)
This booklet constitutes the revised chosen papers of the thirteenth foreign CSI computing device convention, CSICC 2008 hung on Kish Island, Iran, in March 2008. The eighty four usual papers provided 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 concept, SoC and NoC, wireless/sensor networks, video processing and similar themes, processor structure, AI/robotics/control, clinical photo processing, p2p/cluster/grid platforms, cellular advert hoc networks, net, sign processing/speech processing, misc, protection, photo processing functions in addition to VLSI.
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Additional resources for Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers
3 SF Survivor semi-random-restart iteration Fig. 5. Successful operaation of the posed restart algorithm to evade local optimums heuristic is the same as the first one without β modulation, and finally, No GA resstart represents the algorithm witthout any restart process. Table 1 shows a good performance p of our algorithm specially compared with the same procedure without restart algorithm. Furthermore, the average number of inncidents that the introduced Genetic G restart algorithm granted new initial survivor soolution to ACO algorithm is stated in Table 2.
Consider the Uij s corresponding to labeled data equal to 1 and 0 otherwise. Now find a mass prototype for each cluster according to achieved Uij s. Compute fuzzy scatter matrix using Uij s, and find a linear prototype for each cluster. Using μ i s and data points, which are computed in first step find a radius about μ i and find a shell prototype for each cluster accordingly. Form the scatter matrix (SM) for partially labeled data if the ratio of largest eigenvalue to the smallest one, denoted by Rat, was smaller than a threshold then consider the initial value of distance weight to mass prototype relatively small.
In this paper a new approach to fuzzy clustering is proposed, in which during learning a model for each cluster is estimated. Gradually besides, dissimilarity metric for each cluster is defined, updated and used for the next step. In our approach, instead of associating a single cluster type to each cluster, we assume a set of possible cluster types for each cluster with different grades of possibility. Also proposed method has the capability to deal with partial labeled data. Comparing the experimental results of this method with several important existing algorithms, demonstrates the superior performance of proposed method.
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.)