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From Figure 2, it is observed that both the modified method and the conventional method perform better as the SNR increases.
The results showed that both types of SVM method perform better when higher number of input variables are used and the radial basis kernel function performs better compare with the polynomial kernel function.
From the measurement results and error comparison, we can find that the phase-based method and proposed method perform better than the intensity-based method under the nearly focused for optimization, while the proposed method and intensity-based method steadily improves the accuracy with increasing amount of defocusing.
Moreover, for some promiscuous domains, χ2 and SVM-based method perform better than neighbor-counting.
To understand what makes this method perform better, let's compare the ZG04 method with the ST03 method.
Models that were built for substrate/non-substrate and inhibitor/non-inhibitor classification based on the random forest method perform better than those derived using kappa nearest neighbor or support vector machines.
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Although the affine-constraint method performs better than other methods, its performance is not satisfactory for training purposes.
This method performs better and faster than other methods.
Simulation results show the proposed method performs better than other methods.
Results showed that the CNN method performed better than individual intelligent systems performing alone.
Experimental results show that the proposed method performs better than state-of-the-art methods.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com