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On the other hand, the minimum entropy approach iteratively estimates the motion coefficients in sequence via the linear search, which is deemed to involve relatively large computational load.
In terms of further accelerating the minimum entropy approach, parallelized implementation of the linear search within the iteration can be applied as the calculation for each sample is independent with of each other.
The minimum entropy approach performed worse than all other methods; the performance of the method did not vary much when using 30 models or more.
We used the EGFR Boolean model by Samaga et al. and the IL1 model by Ryll et al. to compare the performance of four design strategies: (i) a naive approach choosing experiments at random, (ii) a maximum entropy approach based on a sample of models, (iii) a minimum entropy approach serving as a control and (iv) a maximum difference approach.
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The minimum entropy-based approach [21] for range-bin alignment is an iterative approach which is based on integer shifts of the range profiles, unlike the proposed method and the global range alignment algorithm.
A maximum entropy approach to natural language processing.
We used a multi-threshold version of the maximum entropy approach of (Kapur et al., 1985).
On the other hand, the minimum entropy-based approach may also have problems with local maxima.
Figure 25(b) shows the two range profiles after applying the minimum entropy-based approach for this case.
Figure 22 (a) Range profiles and (b) ISAR image for the real data of the vessel after using the minimum entropy-based approach.
Hence, the ISAR image obtained with the minimum entropy-based approach has a quality poorer than the one in Figure 15(b).
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