Sentence examples for use of multiple kernels from inspiring English sources

The use of multiple kernels results in improved model predictions.

Despite the use of multiple kernels for parallelization of the code, the simulation times for SPECT reconstruction with CPU-based codes are still long.

In the piRNA prediction problem, the use of multiple kernels makes it possible to propose a modular and extensible method.

Some early literature related to use of multiple kernels simultaneously included Hartley and Rao (1967) and Rao (1971) and more recently Bach et al. (2004) and Sonnenburg et al. (2006).

Implementation: We consider data integration via the use of multiple kernel learning supervised learning methods.

In the proposed method, we use an exhaustive search for determining the combination parameters of multiple kernels and the parameter of each kernel.

We developed a machine learning method based on the fusion of multiple kernels and support vector machines (SVMs) (Vapnik, 1995), a well-known machine learning method that has been widely used in diverse areas of bioinformatics.

The use of a multiple kernels approach allows editing, adding or removing piRNA features that can be heterogeneous in a modular manner according to their relevance in a given species.

One useful approach might be that of multiple kernel learning (MKL) [ 36, 42], which uses a few kernels with different covariance structure in a single RKHS model.

By comparing the performances of multiple kernel functions using cross validation on training set, radial basis function (RBF) is selected as the kernel function used in this simulation.

Several methods such as use of multiple Gaussian kernels simultaneously or model averaging have been recommended to overcome these shortcomings (González-Camacho et al. 2012; Tusell et al. 2014).