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We generate a large number of predictive models by applying linear kernel SVR to historical currency rates' bid data for three currency pairs obtained from high-frequency trading.
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We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches.
The data was analysed by applying linear model methods [ 54].
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.
Various types of dividing hyperplanes can be realized by applying a kernel, such as linear, polynomial, Gaussian Radial Basis Function (RBF), or hyperbolic tangent.
Firstly, we reduce the dimension of the entire data (x1 i, x2 i ), i = 1,..., m, by applying kernel PCA with the kernel K given by k1 ⊕ k2.
On the basis of the error range ± 1, length 4 is included in length 3; therefore, Seq3 is defined to have 'C' periods of only 3 and 7. SVM is a non-linear classifier creating a maximum-margin hyperplane by applying a kernel trick to the feature vectors.
By applying the kernel function, the sample is mapped linearly to the high-dimensional feature space.
The nonlinear classification problems can be solved by applying Kernel functions.
Then, specific effects were tested by applying appropriate linear contrasts.
The mapping process is achieved by applying a nonlinear kernel function over each pair of vectors.
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