The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Automatic early stopping using cross validation: quantifying the criteria
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Citations
Cites
A direct adaptive method for faster backpropagation learning: the RPROP algorithm
Riedmiller, M., & Braun, H. (2002) · IEEE International Conference on Neural Networks · doi:10.1109/icnn.1993.298623 · Cited by 3,917
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
(1989) · Cited by 3,214
The Cascade-Correlation learning architecture
Fahlman, S. E., & Lebiere, C. J. (2018) · doi:10.1184/r1/6610403 · Cited by 2,372
Reed, R. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.248452 · Cited by 1,708
(1993) · CaltechAUTHORS (California Institute of Technology) · Cited by 1,498
(1992) · Cited by 1,405
An empirical study of learning speed in back-propagation networks
Fahlman, S. E. (2018) · doi:10.1184/r1/6603266 · Cited by 829
Simplifying Neural Networks by Soft Weight-Sharing
Nowlan, S. J., & Hinton, G. E. (1992) · Neural Computation · doi:10.1162/neco.1992.4.4.473 · Cited by 595
PROBEN 1 - a set of benchmarks and benchmarking rules for neural network training algorithms
Prechelt, L. (1994) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/39794 · Cited by 230
Self-citation
Cited by
Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
fPINNs: Fractional Physics-Informed Neural Networks
Pang, G., Lu, L., & Karniadakis, G. E. (2019) · SIAM Journal on Scientific Computing · doi:10.1137/18m1229845 · Cited by 1,105
Data augmentation: A comprehensive survey of modern approaches
Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891
Super-resolution reconstruction of turbulent flows with machine learning
Fukami, K., Fukagata, K., & Taira, K. (2019) · Journal of Fluid Mechanics · doi:10.1017/jfm.2019.238 · Cited by 837
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., & Couzin, I. D. (2019) · eLife · doi:10.7554/elife.47994 · Cited by 547
Metadata
- Authors
- Prechelt, L.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 1998-06
- Volume
- 11
- Issue
- 4
- Pages
- 761-767
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2016043834
- Cited by
- 990
- References
- 17
- FWCI
- 6.56
- Type
- journal article
- Language
- en
- Open access
- green
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- Prechelt, L. (1998). Automatic early stopping using cross validation: quantifying the criteria. Neural Networks. https://doi.org/10.1016/s0893-6080(98)00010-0
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.
