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The statistical description of sample data is in Table 1.
To avoid the influence of the dispersibility of sample data acquired during normal and various fault conditions, the analysis of the separability of sample data is indispensable.
To this end, we defined special heuristic algorithms on the basis of sample data.
The machine learning model was provided with examples of humpback whale calls and learned how to identify them with reasonable accuracy in a set of sample data.
ξ i is a slack variable, and m is the number of sample data points.
CDF can be estimated efficiently by using a large amount of sample data.
A better CDF estimation can be achieved by using a larger amount of sample data.
Fig. 7 The distribution of sample data point for five types of gestures.
The bigger the fitting error of sample data is, the smaller the weight value of the sample data.
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A similar approach can be applied to evaluate predictive success for out-of-sample data.
Model validation is done with the help of out-of-sample data.
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