Abstract
In this paper, the problem of simultaneously approximating a function and its derivatives is formulated. First, the problem is solved for a one-dimensional input space by using the least square support vector machines and introducing additional constraints in the approximation of the derivative. To optimize the regression estimation problem, we have derived an algorithm that works fast and more accuracy for moderate-size problems. The proposed method shows that using the information about derivatives significantly improves the reconstruction of the function.
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Zhang, R., Liu, G. (2011). Least Square Support Vector Machine for the Simultaneous Learning of a Function and Its Derivative. In: Shen, G., Huang, X. (eds) Advanced Research on Electronic Commerce, Web Application, and Communication. ECWAC 2011. Communications in Computer and Information Science, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20367-1_69
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DOI: https://doi.org/10.1007/978-3-642-20367-1_69
Publisher Name: Springer, Berlin, Heidelberg
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