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This property is also controlled by the type of kernel selected for training and classification purpose, as linear kernel applies linear boundary, Gaussian kernel applies normal distribution boundary while polynomial kernel has capability to evolve convolute boundary to handle the cases where instances from different classes are very mixed up for the given set of features.
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The kernels selected from NPB were set up and executed with 4, 8, 16, 32, 64, and 256 processes in a multiprocessor cluster in order to record communication information during execution (sender node, destination node, timestamp, type of transmission, etc).
LIBSVM [29] is used as the classifier and radial basis function (i.e., Gaussian kernel) is selected as kernel.
SVM was implemented using a package called LIBSVM [ 23], where all the parameters were set as default and Radial Basis Kernel was selected as the kernel.
On the other hand, when multi-resolution and non-linear features were used, linear SVM, polynomial kernel SVM and RBF kernel SVM selected 30, 24 and 46 support vectors, respectively.
Regularized Shannon's delta (RSD) kernel and Lagrange delta sequence (LDS) kernel are selected as singular convolution to illustrate the present algorithm.
The two criteria by which the original kernel is selected are the density of the kernel and the weighted internal and external degrees of it.
From the GE group, the STANDARD kernel was selected for 93 randomly selected subjects and the BONE kernel for the remaining 93 subjects.
The kernel function selected for our experiment is the Gaussian Radial Basis Function (RBF): K X_{i},X_{j}) = e^{-gamma |X_{i}-X_{j}|^{2}/2} (7).
The hyperparameters of each machine learning algorithm (the 'C' regularisation term in the SVM objective function and the gamma term in the RBF kernel) were selected through a coarse grid search in each nested loop.
The kernel type selected is the Radial Basis Function (RBF).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com