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I think you are over complicating this. You can just filter your feature space and fit two separate models. You can make new kernels but I think you described using different kernels for different parts of your feature space, and I cant find anything supporting that decision.


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The RBF kernel is local in the feature space, so it can only work well if a nearest neighbour predictor also works fairly well. It is often worth trying Nearest Neighbour first - if its results are dreadful then I question whether I have the right feature set. But if you are going to use an SVM, it does not feel right to me to start with feature engineering ...


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The idea of using properties of a dataset to decide on classifier characteristics is called metalearning I do not know much about metalearning itself, nor any specific aspects of metalearning for SVMs and classification hardness A search on Google Scholar points to https://link.springer.com/article/10.1007/s10462-013-9406-y (open access) as a recent and well ...


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