On Lagrangian support vector regression

Authors:
S. Balasundaram; Kapil
Affiliations:
School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India;School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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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.