Cross-validation based weights and structure determination of Chebyshev-polynomial neural networks for pattern classification

DOI10.1016/j.patcog.2014.04.026zbMath1373.68362OpenAlexW2008095228MaRDI QIDQ1676889

Lin Xiao, Xiaotian Yu, Yonghua Yin, Yu-Nong Zhang, Dong-Sheng Guo

Publication date: 10 November 2017

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2014.04.026

zbMATH Keywords

robustnessneural networkpattern classificationChebyshev polynomialcross validation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Orthogonal polynomials and functions of hypergeometric type (Jacobi, Laguerre, Hermite, Askey scheme, etc.) (33C45)

Related Items (4)

Multi-variable regression methods using modified Chebyshev polynomials of class 2 ⋮ RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING ⋮ Toward deep neural networks: Mirror extreme learning machines for pattern classification ⋮ Multi-input bio-inspired weights and structure determination neuronet with applications in European central bank publications

Uses Software

• UCI-ml
• LIBSVM

Cites Work

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• Multi-class pattern classification using neural networks
• A survey of cross-validation procedures for model selection
• Pattern classification by linear goal programming and its extensions
• Using Chebyshev polynomials to approximate partial differential equations
• Machine learning for first-order theorem proving
• Extension of Bernstein polynomials to infinite dimensional case
• The Predictive Sample Reuse Method with Applications

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