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• W2184918882 abstract "[8-9] to build a multi-input and single-output nonlinear prediction Support Vector Machines is a new pattern recognition model. The model parameters were tuned by training samples technology which is based on VC Dimension theory of sets and evaluated by the principle of the minimum standard Statistical Learning Theory and Structural Error Minimization deviation. Three original predictive values(hyperbola regression of it [8-9] . According to limited sample information, the model prediction ， exponential regression prediction and grey seeks the optimal approach between complexity and study prediction) were combined to get the prediction results in this ability of the model. As for structural error minimization model. The experimental results showed that this model was far principle, SVM has a better solution to small sample, more superior to other prediction models. nonlinear, high-dimensional learning problems and generalization ability. SVM has provided a new choice for the" @default.
• W2184918882 created "2016-06-24" @default.
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• W2184918882 date "2009-01-01" @default.
• W2184918882 modified "2023-09-27" @default.
• W2184918882 title "based on the rule of structural error minimization, was adopted" @default.
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