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In learning systems with kernels, the shape and size of a kernel plays a critical role for accuracy and generalization. Most kernels have a distance metric parameter, which determines the size and shape of the kernel in the sense of a Mahalanobis distance. Advanced kernel learning tune every kernel’s distance metric individually, instead of turning one global distance metric for all kernels.
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(2017). Local Distance Metric Adaptation. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_484
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_484
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Publisher Name: Springer, Boston, MA
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Online ISBN: 978-1-4899-7687-1
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