Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification.
- DOI
- 10.1162/neco_a_01407
- Published
- 2021 Jul 26
- Container
- Neural computation
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1162/neco_a_01407,
title = {Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification.},
author = {Knoblauch A},
year = {2021},
journal = {Neural computation},
doi = {10.1162/neco_a_01407},
url = {https://doi.org/10.1162/neco_a_01407}
}RIS
TY - JOUR TI - Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification. AU - Knoblauch A PY - 2021 JO - Neural computation DO - 10.1162/neco_a_01407 UR - https://doi.org/10.1162/neco_a_01407 ER -
APA
A, K. (2021). Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification.. Neural computation. https://doi.org/10.1162/neco_a_01407
Source records
- pubmed · retrieved 2026-11-10T08:51:32.179Z