@inproceedings{beee272dd045425e8ceb8a0f5e4a39b5,
title = "Least square support vector machine for the simultaneous learning of a function and its derivative",
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.",
keywords = "SVM, SVR, LS-SVR, regression",
author = "Zhang, {Rui Sheng} and Guozhen Liu",
year = "2011",
doi = "10.1007/978-3-642-20367-1_69",
language = "English",
isbn = "9783642203664",
series = "Communications in Computer and Information Science",
publisher = "Springer, Springer Nature",
pages = "427--433",
editor = "Gang Shen and Xiong Huang",
booktitle = "Advanced research on electronic commerce, web application, and communication",
address = "United States",
note = "International Conference on Advanced Research on Electronic Commerce, Web Application, and Communication ; Conference date: 16-04-2011 Through 17-04-2011",
}