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However, the dataset is not exhaustive.
Furthermore, quantitatively assessing the imaging conditions for each image in a given dataset is not feasible.
AIRSParallel is better than the others when the size of dataset is not so large, and CSCA performs well when the size of dataset is not small.
However, the dataset is not reliable due to numerous data errors in the network.
It is used when the distribution of the original dataset is not known beforehand.
If the dataset is not partitioned too widely, then the training speed is observable.
Also, the reference dataset is not to be mistaken with a training dataset.
The general material recognition dataset is not applicable to FOD material recognition.
Of the datasets collated, only the extremely limited 36S16O2 dataset is not subjected to a detailed analysis.
We notice that the improvement of our method on this dataset is not as overt as on the BRUCE dataset.
Either the kernel space distorts the semantics of the visual space or the dataset is not linear separable in the kernel space, the classification performance cannot be guaranteed.
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