On Learning and Convergence of RBF Networks in Regression Estimation and Classification
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Publication:6184695
DOI10.1007/978-3-030-20912-4_13zbMath1529.68258OpenAlexW2945544107MaRDI QIDQ6184695
Marian A. Partyka, Adam Krzyżak
Publication date: 29 January 2024
Published in: Artificial Intelligence and Soft Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-20912-4_13
Classification and discrimination; cluster analysis (statistical aspects) (62H30) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)
Cites Work
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- Lectures on the nearest neighbor method
- Fourier and Hermite series estimates of regression functions
- Distribution-free consistency results in nonparametric discrimination and regression function estimation
- An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
- On the asymptotic normality of the \(L_2\)-error in partitioning regression estimation
- On radial basis function nets and kernel regression: Statistical consistency, convergence rates, and receptive field size
- Multilayer feedforward networks are universal approximators
- On the strong universal consistency of nearest neighbor regression function estimates
- A distribution-free theory of nonparametric regression
- Convergence and rates of convergence of radial basis functions networks in function learning.
- On deep learning as a remedy for the curse of dimensionality in nonparametric regression
- Consistency of random forests
- Distribution-free consistency of a nonparametric kernel regression estimate and classification
- Nonparametric Regression Estimation by Normalized Radial Basis Function Networks
- Necessary and sufficient conditions for Bayes risk consistency of a recursive kernel classification rule (Corresp.)
- Global convergence of the recursive kernel regression estimates with applications in classification and nonlinear system estimation
- Universal approximation bounds for superpositions of a sigmoidal function
- The rates of convergence of kernel regression estimates and classification rules
- Nonparametric estimation via empirical risk minimization
- Neural Network Learning
- Convergence and Rates of Convergence of Recursive Radial Basis Functions Networks in Function Learning and Classification
- Nonparametric Regression Based on Hierarchical Interaction Models
- Estimation of Dependences Based on Empirical Data
- Asymptotically optimal discriminant functions for pattern classification
- On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
- Learning and Convergence of the Normalized Radial Basis Functions Networks
- Convergence of stochastic processes
- Approximation by superpositions of a sigmoidal function
- Random forests
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