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The low classification error indicates the high level of information gain.
Finally, the low classification accuracy for the classifiers trained on the feature sets derived from solo music database when tested on RWC database indicates that RWC database is a more generalized database with much more variance in the data as compared to the solo music database collected for the current study.
The low classification strengths likely resulted from the facts that: (i) most stream macroinvertebrate taxa show individualistic responses to environmental gradients; (ii) many taxa occur either across all stream types; or (iii) only sporadically in a given stream type; and (iv) only a few species show high fidelity to a given stream type.
Moreover, FMDR only sets one parameter to select the number of best combinations with the low classification error rate, which are then saved into the memory.
Finding a cause for the low classification performance on sample S2 of the proposed algorithm, we randomly pick 10 pairs of non-overlapping read groups from genomes of species which are used in the sample.
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The set with the lowest classification error was chosen.
The k value giving the lowest classification error in CV was selected as the optimal value.
In other words, just because the wine is of the lowest classification doesn't mean it tastes bad.
A track is defined at the lowest classification level and corresponds to a specific series of actions in specific circumstances.
The lowest classification result was obtained with a short hair feature, reaching 53 % accuracy, and the highest classification result was obtained with a "carrying" feature, reaching 75%% accuracy.
Then an AdaBoost process is performed on the labeled target data, and at every boosting iteration, the weak classifier used is selected from the candidate weak source classifiers (found in the previous step) that has the lowest classification error using the labeled target data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com