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Another key parameter in SVR is the kernel parameter.
where is the kernel parameter estimated from the training data.
As a typical global kernel function, the polynomial kernel function can be described as follows: K_{ploy} (varvec{x}_{i},varvec{x}_{j} ) = left( {varvec{x}_{i}^{text{T}} varvec{x}_{j} + 1} right)^{d} (17 where d is the kernel parameter.
And the Gauss kernel function is a typical local kernel function and can be described as follows: K_{RBF} (varvec{x}_{i},varvec{x}_{j} ) = exp left( {frac{{ - left| {varvec{x}_{i} - varvec{x}_{j} } right|}}{{sigma^{2} }}} right) (16 where σ is the kernel parameter.
where C is the cost factor, γ is the kernel parameter, and n is number of labels q j ( j = 1, 2, …, n ), are weight parameters of each class, which set the cost factor C of class j to q, C.
Interestingly, however, it is the kernel parameter estimates which are altered as the latent period is varied with the kernel becoming slightly less local with increasing latent period.
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Apart from the model setup that involves the definition of genomic regions and inclusion or exclusion of some environmental or structural covariates, these parameters are the kernel parameters and the parameters related to the elastic net used in the postprocessing step.
where σ is the kernel width parameter.
Gaussian (RBF): Kleft({x}_i,{x}_kright)= exp left sigma {leftVert {x}_i^T-{x}_krightVert}^2right),kern1.5em sigma >0 (15)where n is the degree of kernel inner product, ( sigma =-frac{1}{2{gamma}^2} ) and γ is the kernel width parameter.
The first one is choosing the kernel parameter to ensure a sufficiently large number of potential support vectors retained in the training sample set.
To see the performance of this particular implementation of (FABS-SVM), the kernel used is radial basis function and the parameters are the following: C value is 10 and the kernel parameter is 1.
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