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pro vyhledávání: '"Burak C. Civek"'
Autor:
Burak C. Civek, Emre Ertin
Publikováno v:
IEEE Transactions on Signal Processing. 70:1256-1269
Publikováno v:
Signal Processing
Highly efficient sequential nonlinear regression algorithms are proposed.Piecewise linear models are used for the nonlinear modeling.Region boundaries are continuously updated according to the data statistics.Second order NewtonRaphson methods are us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2effadb638b15fe1d3ae132521b3ab44
http://arxiv.org/abs/1701.05053
http://arxiv.org/abs/1701.05053
Autor:
Burak C. Civek, Suleyman S. Kozat
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering
We investigate the problem of sequential linear data prediction for real life big data applications. The second order algorithms, i.e., Newton-Raphson Methods, asymptotically achieve the performance of the "best" possible linear data predictor much f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d0395994ac466e95d538722fd256f74
https://hdl.handle.net/11693/37105
https://hdl.handle.net/11693/37105
Publikováno v:
SIU
Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016
Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016
Date of Conference: 16-19 May 2016 Conference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 We present a highly efficient algorithm for the online nonlinear regression problem. We process only the currently av
Publikováno v:
Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016
SIU
SIU
Date of Conference: 16-19 May 2016 Conference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 We propose an efficient method for the high dimensional data regression. To this end, we use a least mean squares (LM
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93ee1affe62e777b9e9740c7085163ee
https://hdl.handle.net/11693/37706
https://hdl.handle.net/11693/37706