The DOI matches its OpenAlex record; 11 of the 11 references it cites were found in a registry or an index.
The Cascade-Correlation learning architecture
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Increased rates of convergence through learning rate adaptation
Jacobs, R. A. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90003-2 · Cited by 1,806
Fast-learning variations on back propagation: an empirical study.
Fahlman, S. E. (1989) · Medical Entomology and Zoology · Cited by 724
Self-citationLearning to Tell Two Spirals Apart
Lang, K. J., & Witbrock, M. J. (1988) · doi:10.13140/2.1.3459.2329 · Cited by 421
Improving the convergence of back-propagation learning with second-order methods
Becker, S., & LeCun, Y. (1989) · Cited by 331
Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization
Watrous, R. L. (1988) · ScholarlyCommons (University of Pennsylvania) · Cited by 242
Consonant recognition by modular construction of large phonemic time-delay neural networks
Waibel, A. H., Sawai, H., & Shikano, K. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1989.266376 · Cited by 153
Fast Learning in Multi-Resolution Hierarchies
Moody, J. E. (1988) · Cited by 147
Efficient Parallel Learning Algorithms for Neural Networks
Kramer, A., & Sangiovanni-Vincentelli, A. L. (1988) · neural information processing systems · Cited by 134
Scaling relationships in back-propagation learning
Tesauro, G. J., & Janssens, B. (1988) · Complex Systems · Cited by 89
Cited by
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Gradient boosting machines, a tutorial
Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,427
Metadata
- Authors
- Fahlman, S. E., & Lebiere, C. J.
- Published
- 2018-06-30
- Volume
- 2
- Pages
- 524-532
- OpenAlex
- W2109779438
- Cited by
- 2,372
- References
- 11
- FWCI
- 27.72
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Cascade-Correlation learning architecture. https://app.cochat.ai/papers/10.1184/r1/6610403
- The paper
- Fahlman, S. E., & Lebiere, C. J. (2018). The Cascade-Correlation learning architecture. https://doi.org/10.1184/r1/6610403
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.
