The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Avoiding false local minima by proper initialization of connections
Abstract
The training of neural net classifiers is often hampered by the occurrence of local minima, which results in the attainment of inferior classification performance. It has been shown that the occurrence of local minima in the criterion function is often related to specific patterns of defects in the classifier. In particular, three main causes for local minima were identified. Such an understanding of the physical correlates of local minima suggests sensible ways of choosing the weights from which the training process is initiated. A method of initialization is introduced and shown to decrease the possibility of local minima occurring on various test problems.
Overview
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
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
Cited by
Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
Neural network studies. 1. Comparison of overfitting and overtraining
Tetko, I. V., Livingstone, D. J., & Luik, A. I. (1995) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00027a006 · Cited by 574
Peng, K., & Zhang, X. (2009) · 2009 Fifth International Conference on Natural Computation · doi:10.1109/icnc.2009.487 · Cited by 20
Metadata
- Authors
- Wessels, L. F. A., & Barnard, E.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1992
- Volume
- 3
- Issue
- 6
- Pages
- 899-905
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2052387497
- Cited by
- 219
- References
- 1
- FWCI
- 10.73
- Type
- journal article
- Language
- en
- Open access
- closed
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: Avoiding false local minima by proper initialization of connections. https://app.cochat.ai/papers/10.1109/72.165592
- The paper
- Wessels, L. F. A., & Barnard, E. (1992). Avoiding false local minima by proper initialization of connections. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.165592
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.
