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We used different types of kernel: linear, polynomial, radial basis function, and multilevel perceptron.
We further used four different types of kernel functions, which are linear, polynomial, Gaussian, and Tanh functions.
We used different types of kernel: linear, polynomial, radial basis function, and also the Bayesian approach on LSSVM.
We made use of leave-one-out cross-validation to assess the accuracy of one-class SVM classifiers using different types of kernel functions.
To give further flexibility in terms of the kernel function, for each individual set of features, we used several different types of kernel matrix.
Results of total accuracies for 9943 sequences with different types of kernel functions and their parameters are summarized in Tables 1, 2 & 3. Further selection search was done for the value of C with 100, 500, and 1000.
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Different types of kernels can be used to train SVMs.
For SVM, different types of kernels such as linear, RBF, polynomial, sigmoid, and histogram intersection (HI) can be used.
Two different yet complementary network properties, i.e., local connectivity and global topological properties are quantified by computing two different types of kernels, i.e., a vector-based kernel and a graph kernel.
Therefore and to maximize the multiprocessor occupancy, five different types of kernels (GPU programs) have been designed to compute different parts of the scenario as shown in Figure 2. The central area is the computational domain containing the scenario that needs to be simulated.
For SVM, we studied different types of kernels and chose the polynomial kernel in this study.
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