Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
