Simplex basis function based sparse least squares support vector regression
作者： Xia Hong , Richard Mitchell , Giuseppe Di Fatta , None
DOI: 10.1016/J.NEUCOM.2018.11.025
关键词:
摘要: Abstract In this paper, a novel sparse least squares support vector regression algorithm, referred to as LSSVR-SBF, is introduced which uses new low rank kernel based on simplex basis function, has set of nonlinear parameters. It shown that the proposed model can be represented linear functions. We propose fast algorithm for solution at cost O(N) by avoiding direct matrix inversion. An iterative estimation been optimize parameters associated with functions aim minimizing mean square errors using gradient descent algorithm. The and are alternatively applied. Finally it dual representation piecewise respect system input. Numerical experiments carried out demonstrate effectiveness approaches.
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