When applied to k-sequences, the evaluation criterion, described in subsection 2 of Methods, works quite well for large k values, typically for k>5, while fpr k< = 5, the flag cost Δc3,1,0 is often much higher than the gain Δc1,M,0 and the overall minimum cost is c0(k).
The numerical tests show our method works quite well.
As compared with Genetic Algorithm (GA), the proposed method worked quite efficiently.
However, conducted analyses of luminance levels in the image limited to eye area shows that proposed method works quite precisely but only for the upper eyelid.
The experimental results show that the frequency tracking method works quite well and fast enough to be used in a real-time controller.
It is seen that this method works quite well and numerical solutions for such problems can be obtained, although it is much slower than Newton's method when employed to consistent, explicit nonlinear equations.
In this section, we introduce the three methods we used as comparisons to our method: K-nearest neighbor method works quite effective as well, the other two fail to satisfy us.
One remarkable thing is that our method works quite well for a very small fraction of training set such as 20% for both abundance estimation and function assignment, and this fact implies that highly interconnected relationship among proteins via complex membership greatly helps us to correctly assign functions.
This is a disadvantage compared to several previous methods for estimating the surface electric field, as there are methods that work quite well with IMAGE data (e.g. Viljanen et al., 2004).
