Results show that the prediction ability of RKHS and RBFNN was superior to that of the BL, indicating that RKHS and RBFNN are able to capture patterns (for example, gene × gene effects) that cannot be captured by a linear model.
It is also shown in the present paper that non-linear response of pixels can be captured by characterizing the linear relation because those heteroscedastic parameters are used to identify source camera device.
Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter.
However, importantly, we found that the semantic similarity structure over all the patterns we observed could not be captured by a simple linear or bilinear function of the population patterns.
We preferred to keep exposure as continuous because: a) it is biologically plausible that increased exposure leads to a steadily increasing risk which would be captured by a log-linear curve, b) using groups will only give better results when the cut-points are selected to break the exposure into substantively different exposure levels.
This convergence of parallel pathways with markedly different stimulus-processing characteristics can be captured by models with several linear filters in parallel.
Still, those enzymes with particularly low vmax, as identified in this work, may very well be excellent candidates for those that exert the greatest degree of control over flux, but then the situation becomes continuous and non-linear, and cannot be captured by simply identifying a single enzyme with the lowest (in vitro) vmax.
It assumes that most non-linear profiles can be captured by a combination of two polynomial powers [ 28].
In this way, the fast and simple calculation steps of linear methods are retained, and the nonlinear features can be captured by the kernel-based algorithms.
Many of the properties of real cortical neurons can be captured by making the equation for the recovery variable of the FHN equations quadratic (instead of linear).
