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Inspired from the adaptive kernel method for probability density function estimation, the first stage of the approach defines a pattern of thresholds corresponding to the various training samples and these thresholds are later used to derive the decision rule.
Like the adaptive kernel methods, instead of defining a general unique threshold as seen with several classical AD approaches, the proposed approach allocates a set of thresholds corresponding to the various training samples.
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The various choices of training samples may result in different estimated parameters, leading, in turn, to incompatible results.
This backward approach for refining a series of highly discriminative gene clusters for classification purpose proves to be very consistent and stable when applied to various types of training samples.
To select various RBF parameters, the class membership information of training samples is utilized to produce new cluster classes.
Experiments were performed to verify the various research methods by using 300 transformers as training samples and 200 transformers as test samples.
The classification OA is plotted under various percentages of samples in Fig. 4a, where the x-axis denotes the percentage of training samples per class and the y-axis is OA averaged over five times.
The classification OA is plotted under various percentages of samples in Fig. 7b, where the x-axis denotes the percentage of training samples per class and the y-axis is averaged OA over five times.
The use of training samples from a large class of ncRNAs from diverse organisms enabled us to find different categories of ncRNAs from various organisms.
He visits various training camps each preseason.
Various training will be provided.
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