On the Cesàro-Means-Based Orthogonal Series Approach to Learning Time-Varying Regression Functions
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Publication:2814132
DOI10.1007/978-3-319-39384-1_4zbMath1358.68234OpenAlexW2506584597MaRDI QIDQ2814132
Piotr Duda, Maciej Jaworski, Adam Krzyżak, Lena Pietruczuk
Publication date: 17 June 2016
Published in: Artificial Intelligence and Soft Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-39384-1_4
Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05)
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Cites Work
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- Centroid of triangular and Gaussian type-2 fuzzy sets
- Nonparametric identification of quasi-stationary systems
- The CART decision tree for mining data streams
- Parallel Approach to the Levenberg-Marquardt Learning Algorithm for Feedforward Neural Networks
- Distribution-free consistency of a nonparametric kernel regression estimate and classification
- On nonparametric identification with prediction of time-varying systems
- Real-time identification of time-varying systems by non-parametric algorithms based on Parzen kernels
- Nonparametric fitting of multivariate functions
- On Bayes Risk Consistent Pattern Recognition Procedures in a Quasi-Stationary Environment
- On-line identification of time-varying systems by nonparametric techniques
- Sequential Estimates of Probability Densities by Orthogonal Series and Their Application in Pattern Classification
- Artificial Intelligence and Soft Computing - ICAISC 2004
- Artificial Intelligence and Soft Computing - ICAISC 2004
- The rates of convergence of kernel regression estimates and classification rules
- Application of multiple Fourier series to identification of multivariable non-stationary systems
- On design of flexible neuro-fuzzy systems for nonlinear modelling
