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The iteration in which maximum classification performance occurs is considered as optimum iteration.
Very high or close to maximum classification performance was attained below a time constant of 20 ms in most cases and for all stimulus types that were investigated here.
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A principal components analysis was used to reduce the dimensionality of this matrix, and the first 6 semantic-feature components were selected based on maximum model classification performance for the time window 100 300 ms using an independent group of participants.
The number of components used in the final model was determined based on maximum model classification performance for data in the time window 100 300 ms using an independent group of participants performing the same task with similar objects (see Clarke et al. 2013).
Material classification performance experiments were also performed.
We propose here three different back-end systems to combine, smooth, and improve the classification performance of the FA: 1. Maximum a posteriori (MAP): This well-known method has been widely used in the literature [40],[40].
However, it seems that the level threshold corresponding to the maximum log-condition number indicate the optimal or near optimal level classification performance in our experiment.
Our approach showed better classification performance as estimated by different indicators (Table 4), including the maximum AUC.
The classification performance of the proposed method is compared to the existing methods based on the maximum likelihood classification with (1) fully polarimetric SAR data (received power), (2) the received + H + A + , (3) the received power + odd + even + diffuse of the scattering components.
classification performance.
Hence, in the classification phase, the classification performance is improved.
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