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Coefficient-Based Regression with Non-Identical Unbounded Sampling
2013
Abstract and Applied Analysis
We investigate a coefficient-based least squares regression problem with indefinite kernels from non-identical unbounded sampling processes. Here non-identical unbounded sampling means the samples are drawn independently but not identically from unbounded sampling processes. The kernel is not necessarily symmetric or positive semi-definite. This leads to additional difficulty in the error analysis. By introducing a suitable reproducing kernel Hilbert space (RKHS) and a suitable intermediate
doi:10.1155/2013/134727
fatcat:tdccsw6fsrerldlut6act7mvw4