Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension

17 Mar 2020 Hamm Thomas Steinwart Ingo

We derive improved regression and classification rates for support vector machines using Gaussian kernels under the assumption that the data has some low-dimensional intrinsic structure that is described by the box-counting dimension. Under some standard regularity assumptions for regression and classification we prove learning rates, in which the dimension of the ambient space is replaced by the box-counting dimension of the support of the data generating distribution... (read more)

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  • STATISTICS THEORY
  • STATISTICS THEORY