Regression function and noise variance tracking methods for data streams with concept drift
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Publication:1797885
DOI10.2478/amcs-2018-0043zbMath1404.62039OpenAlexW2895158127MaRDI QIDQ1797885
Publication date: 22 October 2018
Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.2478/amcs-2018-0043
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Cites Work
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- Difference-based variance estimation in nonparametric regression with repeated measurement data
- Nonparametric orthogonal series estimators of regression: A class attaining the optimal convergence rate in \(L_ 2\)
- Nonparametric smoothing and lack-of-fit tests
- The pointwise rate of convergence of the kernel regression estimate
- A distribution-free theory of nonparametric regression
- Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks
- How to adjust an ensemble size in stream data mining?
- Variance estimation in nonparametric regression via the difference sequence method
- Survival analysis on data streams: analyzing temporal events in dynamically changing environments
- Almost everywhere convergence of a recursive regression function estimate and classification (Corresp.)
- A Flexible and Fast Method for Automatic Smoothing
- Non-parametric modelling of time-varying customer service times at a bank call centre
- Nonparametric System Identification
- Residual variance and residual pattern in nonlinear regression
- Reduction of distributed system identification complexity using intelligent sensors
- Efficient estimation of conditional variance functions in stochastic regression
- Local Polynomial Variance-Function Estimation
- Nonparametric Estimation of Covariance Structure in Longitudinal Data
- A survey on concept drift adaptation
- Generalized Kernel Regression Estimate for the Identification of Hammerstein Systems
- Graphics processing units in acceleration of bandwidth selection for kernel density estimation
- On Estimation of a Probability Density Function and Mode
- Distribution of the Ratio of the Mean Square Successive Difference to the Variance
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