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The use of a kernel simplifies the design of embedded systems because it allows the system to be divided into multiple independent elements called tasks.
A key part of our approach is the use of a kernel that compares sequences of feature vectors and produces a measure of similarity.
Thus, a simple sampling scheme of training data is adopted, and the use of a kernel method becomes feasible.
Kernel PCA, as a nonlinear extension of PCA, was developed to explore the nonlinear relationship among variables by the use of a kernel function.
The SVM [29, 30] is a linear machine, working in the high-dimensional feature space formed by the non-linear mapping of the N-dimensional input vector x into an L-dimensional feature space (L > N) through the use of a kernel function K x,x i ).
It makes use of a kernel density estimate classifier to generate read pileups representing genomic regions that are targeted by an RBP.
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A novel aspect of our feature extraction procedure was the use of a kernel-based principal components analysis because the captured real-time data exhibited significant nonlinearity.
The protocol also makes use of a kernel-based link quality estimator which allows the choice of the most suitable Gateways, providing load-balancing and disconnection prediction in each cluster.
An alternative approach to allow for rare genetic variants within a functional unit or genomic region to have different direction and/or magnitude of effects on a complex trait is to make use of a kernel-based adaptive cluster (KBAC) [ 30], which categorises individuals according to 'genotype groups'.
This would mean the two classes of data can become nonseparable, requiring the use of a nonlinear kernel (e.g., an RBF kernel), with an associated validation study to find the appropriate value of the kernel parameter.
The use of such a kernel has been shown to minimize the risk of false positive findings [35].
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