Exact(1)
The corner estimation or the prediction stage has been improved by utilising more efficient voting process using a single kernel density estimation.
Similar(59)
Empirical computation time was obtained on a dual core AMD Opteron tm) Processor 280, 2.4 GHz, 2 GByte RAM, Java Runtime Environment 1.6, Linux kernel 2.6.18, using a single core.
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.
As a baseline we computed optimal AUC scores using a single Gaussian kernel: first on sequence data alone, then on sequence and secondary structure concatenated together using encoding and window length parameters described in [38] and finally on a variant of [38] using a grid search to find optimal extension length parameters.
In the CUDA version all possible kernel combinations are implemented using a single file with templates.
In recent years, several methods have been proposed to combine and use multiple-kernel matrices instead of using a single one.
The first version used CPU loops and two GPU kernels, whereas the second used a single GPU kernel and GPU shared memory.
Using a single booster.
Six virtual cohorts were constructed for analysis: four cohorts using scans reconstructed with a single kernel (b31f Siemens (n = 183), b45f Siemens (n = 183), STANDARD GE (n = 186), BONE GE (n = 186)) and two mixed cohorts.
Our preliminary experiments show that when using KL to combine a single kernel and a single Laplacian, its performance strongly depends on the quality of the kernel and the Laplacian, which results in a model selection problem to determine the optimal settings of the kernel and the Laplacian.
Figure 8 displays the obtained running time in milliseconds for a single kernel measured using different resolutions with bandwidth parameters h s = 0.02 and h s = 0.05.
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