Sentence examples for kernel methodology from inspiring English sources

The empirical results reported in this paper show that our strategy of using kernel methodology for an inverse-Quantitative Structure-Activity Relationship is sufficiently powerful to find a meaningful solution for practical problems.

Kernel PCA methodology, an elegant nonlinear generalization of the linear PCA, is illustrated by considering the examples of (i) denoising chaotic time series and, (ii) prediction of properties of polymer nanocomposites developed in our laboratory.

Damoulas and Girolami [ 15] offered the kernel combination methodology for the prediction of protein folds and the best accuracy was 70%.

In this supplement, Reverter et al. [ 35] propose a kernel PCA methodology that first selects the appropriate kernel for each data type and second combines the kernels from the different data types for a given statistical task.

Another advantage of the kernel-based methodology is that the local linear model has a built in edge correction.

The kernel of the methodology is constituted by a set of optimization strategies and a module, named Life Cycle Mapping (LCM).

A simple methodology for kernel creation based on the periodicities in time series data is proposed.

The data are shown using the Kernel Density Estimation (KDE) methodology, which plots the detrital dates as a set of Gaussian distributions (Vermeesch 2012).

Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level.

Although it seems there is no connection between these two methodologies, both kernel methods and rough sets explicitly or implicitly dwell on relation matrices to represent the structure of sample information.

This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation.