Domain independent data discrepancy detection using ensemble learning
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
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Kernel regression is one model that has been applied to explain or design radial-basis neural networks. Practical application of the kernel regression method has shown that bias errors caused by the boundaries of the data can seriously effect the accuracy of this type of regression. This paper investigates the correction of boundary error by substituting an asymmetric kernel function for the symmetric kernel function at data points close to the boundary. The asymmetric kernel function allows a much closer approach to the boundary to be achieved without adversely effecting the noise-filtering properties of the kernel regression.