In order to keep the number of calls to the accurate flow simulator computationally tractable, a recent distance-based approach relying on fast proxy simulations is revisited, and turned into a non-stationary kriging method where the covariance kernel is obtained by combining a classical kernel with the proxy.
Multiple Kernel Learning (MKL) aims to seek a better result than single kernel learning by combining a compact set of sub-kernels.
Recently, a new algorithm, Kernel Laplacian (KL) clustering, is proposed to combine a kernel and a Laplacian simultaneously in clustering analysis (Wang et al., 2009).
De los Campos et al. [ 14] suggest an interesting venue for further research on the use of KRR for prediction in quantitative genetics, by combining multiple kernels in a single model, each kernel representing a single variance component (e.g., additive, dominance, or epistasis).
By combining resistant kernel and factorial least-cost path modelling we mapped core areas and the main linkages among them, and identified several key pinch points that may limit regional connectivity of the population.
Because maximum dispersal distance quickly saturated over time (see results), we calculated the hypothetical plant recruitment kernels generated by each individual lizard (for a given, ingested seed) by combining kernel density estimates, habitat structure and the relative probabilities of surviving predation, germinating and establishing at the various microhabitats.
By combining linear kernel-PCA with a quick-to-solve optimization problem, it couples the fast run time and robustness of PCA with the biological interpretability of Q-matrices obtained from model-based approaches such as STRUCTURE and ADMIXTURE.
In Reference [111], a two-stage neural network architecture constructed by combining RNN with kernel feature extraction is proposed for stock prices forecasting.
We develop an algorithm to filter them out by combining multiple kernels.
By combining the Linux kernel with the extant GNU programs — text editor, compiler, debugger, and many other tools and utilities, many of which Stallman wrote himself — it again became possible to run a computer using only freely modifiable, shareable software: the GNU/Linux system, now often known simply as Linux, though the full term is more accurate.
