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Our main independent variable was a composite measure of the food environment calculated as the difference between the kernel densities of healthful and unhealthful retail food vendors at the census block centroid of each individual's residence.
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The key difference between the kernels is the thickness of the tails (i.e. the probability of transmission at longer distances), which are much higher for the fat-tailed and FMD kernels than for the Gaussian or exponential ones (Figure 2B).
In the right image, the method is used to exaggerate the variations, with subtle differences between the kernels and the row misalignment highlighted.
The differences between the kernels are clearly observable from Figure 4.
Following the recommendations in [ 20] for comparing multiple classifiers on multiple data sets, we ranked the kernels for each data set and used nonparametric tests to assess differences between the kernels.
The interpretation of global changes at the kernel level was further complicated by large differences between the different kernel parts, ABA being 5 to 60 times more concentrated in the embryo than in the endosperm (Table 1).
We find that on the SRDS1 versions of the datasets, both the LA and the CTD methods significantly outperform the 5-spectrum kernel and that there are no statistically significant differences between the LA kernel and the CTD classifier.
In the case of the SRDS1 and the SRDS2 datasets, we find that the performance of both the LA kernel and the CTD classifiers is significantly better than that of the 5-spectrum kernel classifier and that there are no significant differences between the LA kernel and the CTD classifiers.
In general, we see a significant difference between the MKL-weighted kernels and their individual base kernels.
What is the difference between the PSF and the kernel?
A formula for the index of the operator, i.e., the difference between the dimensions of the kernel and cokernel is obtained.
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