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KDE uses a kernel function to smooth the local neighborhood of data samples.
A SVM classifier uses a kernel function to make a transformation over the data and change the workspace, separating relevant from nonrelevant documents.
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We therefore briefly discuss implications and problems that might emerge using a kernel function that does not satisfy the theorem.
Fortunately, kernel functions that do not satisfy Mercer's theorem have been verified to be effective for classification of sequential data using a kernel function in [18].
The kernel method inserts the cross-correlation term using a kernel function whereas ANSS considers the cross-correlation term by averaging on a few frames.
In this study, we used a kernel function (3) that is a particular case of the multiform, tiltable exponential kernel proposed in[23].
Then, in the second step, we use a kernel function to map each VSMMD to a feature space typically used for classification.
The algorithm can use a kernel function to map the problem into a high dimensional space where the problem can be easier to solve.
In order to solve non-linear problems, SVMs use a kernel function [17, 18], which allows better fitting of the hyperplane to more general datasets.
In this technique, the input data, x, are mapped from original space into a multidimensional feature space using a kernel function and then, a linear fitting function in this space is computed.
On the basis of the theory of the statistical learning, SVM is proposed as a general machine learning method to map the input data space using a kernel function to a high- dimensional space (Wei et al. 2011).
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