Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 37, Issue 12, Pages 8784-8792Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.06.028
Keywords
Lagrangian support vector machines; Support vector regression; Time series
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Prediction by regression is an important method of solution for forecasting. In this paper an iterative Lagrangian support vector machine algorithm for regression problems has been proposed. The method has the advantage that its solution is obtained by taking the inverse of a matrix of order equals to the number of input samples at the beginning of the iteration rather than solving a quadratic optimization problem. The algorithm converges from any starting point and does not need any optimization packages. Numerical experiments have been performed on Bodyfat and a number of important time series datasets of interest. The results obtained are in close agreement with the exact solution of the problems considered clearly demonstrates the effectiveness of the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.
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