The phrase "as a kernel function" is correct and usable in written English.
It can be used in contexts related to mathematics, computer science, or machine learning, particularly when discussing algorithms or methods that utilize kernel functions.
Example: "In support vector machines, data is transformed into a higher-dimensional space as a kernel function to improve classification accuracy."
Alternatives: "in the role of a kernel function" or "functioning as a kernel function".
Integral type non-local elastic models phenomenologically incorporate microstructure information through a weighting function known as a kernel function.
In this study, Radial Basis Function (RBF) is preferred as a kernel function due to its flexibility to observations from many divers fields.
The SVM classifier is designed by selecting Gaussian RBF kernel as a kernel function.
First, we introduce an LCSS kernel as a kernel function that does not satisfy Mercer's theorem.
The training and probe data were identical to the proposed method, and we used a linear function as a kernel function.
Fröhlich et al. introduced the concept of the optimal assignment in the field of cheminformatics as a kernel function for attributed molecular graphs [29, 30].
In SVMs, because all computations on the input data are written as inner products, a kernel function is typically used to efficiently compute inner products in a high-dimensional space.
The premise is, in the absence of one class's training patterns, to treat the origin of the higher dimensional space identified through the choice of a kernel function as the only member of the opposite class.
Mode calculation is formulated as an iterative scheme of mean calculation, which takes a certain number of feature points and calculates their weighted mean value by using a kernel function, such as the Gaussian.
They can be also classified into two kind of definitions: (1) the product of the local form of a signal with a kernel function, such as STFT, WVD, etc; (2) the product of the signal and a local kernel function, such as WT, Gabor transform, etc.
Thus, a kernel function, such as the Gaussian kernel used in this study [ 35], was used to map the abstracts into a high dimensional space such that the positive and negative support vectors became separable by a straight line, which is known as a hyperplane in the multi-dimensional space (see right of Figure 6).
