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Citrus quality classification is an important and widely studied topic since it has significant role in its market price determination. Due to citrus quality indicators series nonlinearity and no-stationary, the accuracy of conventional mostly used methods including linear discriminant analysis, K-means clustering and neural network has been limited. The use of support vector machine (SVM) has been...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
The generalization error of support vector machine usually depends on its kernel parameters, but there is no analytic method to choose kernel parameters for SVM. In order to choose the kernel parameters for SVM, the simulated annealing algorithm and genetic algorithm are combined, which is called simulated annealing genetic algorithm (SA-GA), to choose the SVM kernel parameters. SA-GA makes use of...
Robust object tracking is quite important in computer vision. In this paper, a novel tracking approach for single object which combines genetic algorithm and Kalman filter is proposed. Genetic algorithm is introduced and reasonably applied to find the tracked object in a search area. A further step called multi-blocks voting is exploited for obtaining more accurate object localization. Kalman filter...
A key step in program performance optimization is to determine optimal values for certain parameters. Static approaches determine these values based on analytical models. However, complex computer architectures and complex code structures limit the strength of them. Execution-driven approaches like iterative compilation determine these parameter values by executing the program with different parameter...
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