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In addition, direct use of forest inventory data relies on subjective classification of decay class that can differ from one-field-crew to the next, so utilizing decay and transfer algorithms may reduce the potential effect of these classifications.
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We have gathered statistics from this deployment regarding the frequency of both variant classification changes and the effects of these classification changes on patients.
Moreover, the time-frequency domain features can also help improve the effect of the classification.
As we had, somehow, three distinct population from Kathmandu municipality, Kathmandu VDCs and Dhading VDCs; effect of this classification as an effect modifier would be worth analysing.
To determine the effect of the classification accuracy on the number of nearest neighbors (k), the classifications were performed with k varying from three to eight.
We conducted some additional experiments in order analyze the effect of these on the classification accuracy.
Similar to the classical testing ANOVA, the total observed variation in the response variable is explained as the sum of observed variation due to the effects of the classification variable and the observed variation due to random error.
Subgroup analyses will include effects of the classification as matched vs. mismatched decisions for patients in both the IG and the CG.
To further determine the effect of these two measures on the classification performance, we removed each measure from the model one at the time and then recalculated the accuracy scores.
We plan to further investigate the effect of these errors on our correlation and classification results.
All these techniques have given a level of sophistication to lymphoma classification, which has been helpful for planning treatment and trials, but has had the less welcome effect of making previous classifications redundant, and to some extent irrelevant.
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