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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.
Else, the set Sn−2 with lowest classification error was chosen, and the process was repeated until the classification error did not drop further.
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.
A rank of 12 resulted in the lowest classification error for cross validated samples, and the performance of the classification model is summarized in Table 2.
In covert attention mode, centrally located electrodes yield the lowest classification error.
Similar(51)
The lowest classification errors for the PCA LDA method equalled 3.8% and were obtained for the measurements acquired at the wavelength interval of 10 and 30 nm.
The low classification error indicates the high level of information gain.
A good choice of parameters is characterized both by low feedback power (small (|alpha|^{2})) and high input rate (low sample time Δt) while still resulting in a low classification error rate.
However, in earlier iterations, these single feature weak classifiers are capable of achieving a low classification error rate of 10%30%% [7] while in later iterations, cannot achieve less than 40%50%% error rate.
The window size was optimized for low classification error.
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Justyna Jupowicz-Kozak
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