Generalization ability of fractional polynomial models
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Publication:461189
DOI10.1016/j.neunet.2013.09.009zbMath1296.68133OpenAlexW2007556383WikidataQ50714588 ScholiaQ50714588MaRDI QIDQ461189
Yiming Ding, Yunwen Lei, Lixin Ding
Publication date: 10 October 2014
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.neunet.2013.09.009
Point estimation (62F10) Learning and adaptive systems in artificial intelligence (68T05) Stochastic approximation (62L20)
Uses Software
Cites Work
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- Another look at statistical learning theory and regularization
- Optimal learning rates for least squares regularized regression with unbounded sampling
- Convergence rates of generalization errors for margin-based classification
- Decision theoretic generalizations of the PAC model for neural net and other learning applications
- Generalization bounds for function approximation from scattered noisy data
- Sphere packing numbers for subsets of the Boolean \(n\)-cube with bounded Vapnik-Chervonenkis dimension
- A distribution-free theory of nonparametric regression
- The covering number in learning theory
- Statistical behavior and consistency of classification methods based on convex risk minimization.
- Practical selection of SVM parameters and noise estimation for SVM regression
- Analysis of convergence performance of neural networks ranking algorithm
- On the mathematical foundations of learning
- Learning Theory
- Universal approximation bounds for superpositions of a sigmoidal function
- VC Dimension and Uniform Learnability of Sparse Polynomials and Rational Functions
- Separable nonlinear least squares: the variable projection method and its applications
- 10.1162/153244302760200713
- Comparison of Model Selection for Regression
- The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate
- Convexity, Classification, and Risk Bounds
- Theory of Reproducing Kernels
- Convergence of stochastic processes
- The elements of statistical learning. Data mining, inference, and prediction
- Model selection and error estimation
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