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On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2016) 9693 37-48
DOI: 10.1007/978-3-319-39384-1_4
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Abstract

In this paper an incremental procedure for nonparametric learning of time-varying regression function is presented. The procedure is based on the Cesàro-means of orthogonal series. Its tracking properties are investigated and convergence in probability is shown. Numerical simulations are performed using the Fejer’s kernels of the Fourier orthogonal series.

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Duda, P., Pietruczuk, L., Jaworski, M., & Krzyzak, A. (2016). On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9693, pp. 37–48). Springer Verlag. https://doi.org/10.1007/978-3-319-39384-1_4

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