Sentence examples for use of the kernel from inspiring English sources

Furthermore, its extension from linear models to non-linear ones is also largely direct via the use of the kernel trick.

The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA).

Larger neighborhoods were chosen for this analysis to reflect more distant sources; however, values at greater distances were down-weighted due to use of the kernel density function.

The use of the kernel trick enables SVMs to map the data into high-dimensional space and very efficiently perform nonlinear classification and regression (Ben-Hur et al. 2008).

One unique characteristic as a specific type of learning algorithm is that it is characterized by the capacity control of the decision function, the use of the kernel functions, and the sparsity of the solution [ 10].

We then get first the prediction distribution, i.e. the distribution of X t k given the data Y1: k -1 (and t1: M ), by use of the kernel K t k - 1, t k : (7) P (X t k ∈ B | Y 1 : k - 1 = y 1 : k - 1, T 1 : M = t 1 : M ) = ∫ B ∫ X t k - 1 f X t k - 1 | Y 1 : k - 1, T 1 : M x t k - 1 | y 1 : k - 1, t 1 : M × d L X t k - 1 x t k - 1 K t k - 1, t k x t k - 1, d x t k for each set B ∈ B X t k.

Least squares support vector machines (LSSVM) with Gaussian kernel represent the most used of the kernel methods existing in the literature for regression and time series prediction.

The authors state that end-use of the kernel is clearly influencing the physio-chemical kernel characteristics implying properties such as milling quality, kernel hardness, and kernel protein content.

The use of the Gaussian kernel function is a robust selection; however, other kernel functions may be more appropriate for the specific data type, particularly if there are no adjustments for covariate data.

These programs make use of the powerful kernel of Linux to generate packets at speeds of up to one Gigabit per second.

By making use of the resolvent-kernel technique, the governing equation is solved and the solution to the original problem is presented in explicit form.