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Results are robust to different kernel methods.
fitting point kernel methods were done for DLEBF computation.
Graph kernel methods seem promising, which, however, are not interpretable.
To simplify the computation, kernel methods can be used [43].
Broadly, neural computation includes kernel methods, such as SVM.
In this paper, we are concerned with kernel methods for automatic WSD.
Kernel methods such as support vector machines are a powerful technique to solve pattern recognition problems.
The success of kernel methods is very much dependent on the choice of kernels.
We choose the parameters for other kernel methods in similar way.
The widely used kernel methods offer efficient similarity measurements between two SVM supervectors.
Moreover, traditional kernel methods can also be applied to ELM [15].
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