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The SVM based approach can alleviate the overfitting problem encountered in the MOR model for learning the mixture coefficients, and greatly increases the detection and learning speed.
The problem is how to estimate the mixture coefficients.
The following part will show the estimation method of the mixture coefficients.
In order to make decisions, the experts are weighted by the mixture coefficients.
Both AdaBoost.M1, which was employed here, and AdaBoost.M2, are using greedy optimisation for the mixture coefficients.
For unresolved T-F unit, computation of the mixture coefficients is different from resolved.
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In DHF, we expect that the second mixture coefficient of DHF could be larger than others.
Meanwhile, the Gaussian mixture coefficient controls the peak height of DHF.
Formula (10) shows that the th mixture coefficient depends on the appearance of the th or th harmonic.
Analysis above implies that the computation of mixture coefficient has to combine the information of other channels.
According to analysis above, the mixture coefficient of DHF for resolved T-F unit is computed as follows: (8).
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