The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings
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Citations
Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Self-citationApproximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
Vapnik, V. N., & Chervonenkis, A. Y. (1971) · Theory of Probability & Its Applications · doi:10.1137/1116025 · Cited by 2,448
Cited by
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
Neural Networks: A Review from a Statistical Perspective
Cheng, B., & Titterington, D. M. (1994) · Statistical Science · doi:10.1214/ss/1177010638 · Cited by 1,048
Credit risk assessment with a multistage neural network ensemble learning approach
YU, L., WANG, S., & LAI, K. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.01.009 · Cited by 310
Machine learning for medical ultrasound: status, methods, and future opportunities
Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262
Revisiting squared-error and cross-entropy functions for training neural network classifiers
Kline, D. M., & Berardi, V. L. (2005) · Neural Computing and Applications · doi:10.1007/s00521-005-0467-y · Cited by 237
Metadata
- Authors
- White, H.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 1990-01
- Volume
- 3
- Issue
- 5
- Pages
- 535-549
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2108959409
- Cited by
- 679
- References
- 4
- FWCI
- 20.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings. https://app.cochat.ai/papers/10.1016/0893-6080(90)90004-5
- The paper
- White, H. (1990). Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings. Neural Networks. https://doi.org/10.1016/0893-6080(90)90004-5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
