On the global convergence of the Parzen-based generalized regression neural networks applied to streaming data. (English) Zbl 1508.68314
Rutkowski, Leszek (ed.) et al., Artificial intelligence and soft computing. 17th international conference, ICAISC 2018, Zakopane, Poland, June 3–7, 2018. Proceedings. Part I. Cham: Springer. Lect. Notes Comput. Sci. 10841, 25-34 (2018).
Summary: In the paper we study global (integral) properties of the Parzen-type recursive algorithm dealing with streaming data in the presence of the time-varying noise. The mean integrated squared error of the regression estimate is shown to converge under several conditions. Simulations results illustrate asymptotic properties of the algorithm and its convergence for a wide spectrum of a time-varying noise.
For the entire collection see [Zbl 1387.68028].
For the entire collection see [Zbl 1387.68028].
MSC:
| 68T09 | Computational aspects of data analysis and big data |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68W27 | Online algorithms; streaming algorithms |
Keywords:
stream data mining; Parzen-type estimator; global convergence; generalized regression neural networkReferences:
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