Sentence examples for multiple kernel coefficients from inspiring English sources

After that, we propose an alternative algorithm with proved convergence to identify the multiple kernel coefficients.

It is worth noting that in multiple kernel learning, the rank of coefficients are only moderately correlated with the rank of individual performance.

For example, in multiple kernel learning, a kernel is constructed from each view and a set of kernel coefficients are learned to obtain an optimal combined kernel matrix.

Instead of learning one kernel, multiple kernel learning assumes the kernel is comprised of a linear combination of multiple predefined base kernels.

Multiple kernel learning (MKL, see [28]) was used to combine the kernels as input for IOKR.

Many researchers have studied multiple kernel learning (MKL) algorithms [19 21].

They proposed an algorithm based on multiple empirical kernel which maps data explicitly in multiple kernel spaces.

Unlike the traditional Multiple Kernel Learning (MKL) with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps the original data space into multiple feature spaces via different empirical kernels.

Multiple kernel learning (MKL) aims at learning a combination of different kernels in order to better match the underlying problem instead of using a single fixed kernel.

Multiple Kernel Learning (MKL) aims to seek a better result than single kernel learning by combining a compact set of sub-kernels.

Multiple kernel learning allows for an optimal kernel function to be learned in a computationally efficient manner.