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We consider the parameter tuning problem for Gaussian-kernel support vector machines, i.e., how to set its two hyperparameters — σ (bandwidth) and C (tradeoff). Among the many methods in the literature, the majority handle this task by maximizing the cross validation accuracy over the first quadrant of the (σ, C) plane. However, they are all computationally expensive because the objective function...
Data sets are often modeled as samples from some probability distribution lying in a very high dimensional space. In practice, they tend to exhibit low intrinsic dimensionality, which enables both fast construction of efficient data representations and solving statistical tasks such as regression of functions on the data, or even estimation of the probability distribution from which the data is generated...
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