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by ibgeek
212 days ago
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I'm not sure if I'm understanding correctly, but it reminds me of the kernel trick. The distances between the training samples and a target sample are computed, the distances are scaled through a kernel function, and the scaled distances are used as features. https://en.wikipedia.org/wiki/Kernel_method |
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