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This paper proposes a method to simultaneously select the most relevant single nucleotide polymorphisms (SNPs) markers — the attributes — for the characterization of any measurable phenotype described by a continuous variable using support vector regression (SVR) with Pearson VII Universal Kernel (PUK). The proposed study is multiattribute towards considering several markers simultaneously to explain...
The support vector machine is a powerful supervised learning algorithm that has been successfully applied to a plenty of fields including text and image recognition, medical diagnosis and so on. The kernel and its parameters optimization, formally known as model selection, is a crucial factor which influences a good tradeoff between bias and variance. To automate model selection of support vector...
The main purpose of evaluation model of teaching quality is to help improving teaching. The rationality and fairness of conventional models have been doubted, as evaluation results are obtained using subjective weights. Therefore, a more scientific and reasonable evaluation model is needed. This paper introduces an evaluation model of teaching quality based on genetic algorithm. Objective weights...
The genetic simulated annealing algorithm can get global solution with low computational load. By means of this algorithms optimization method, the support vector machines (SVM) radial basis probabilistic kernel parameters of the performance was found out. A special software was developed on this method, it can be used in different field and improved the application of SVM in industry area. Then,...
This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if...then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheless, the performance of these approaches is usually affected by their uncertain parameters. The proposed...
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