dorsal/arxiv
View SchemaFastest learning in small world neural networks
| Authors | D. Simard, L. Nadeau, H. Kröger |
|---|---|
| Categories | |
| ArXiv ID | physics/0402076 |
| URL | https://arxiv.org/abs/physics/0402076 |
| DOI | 10.1016/j.physleta.2004.12.078 |
| Journal | Phys. Lett. A336 (2005) 8-15. |
Abstract
We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition.
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"abstract": "We investigate supervised learning in neural networks. We consider a\nmulti-layered feed-forward network with back propagation. We find that the\nnetwork of small-world connectivity reduces the learning error and learning\ntime when compared to the networks of regular or random connectivity. Our study\nhas potential applications in the domain of data-mining, image processing,\nspeech recognition, and pattern recognition.",
"arxiv_id": "physics/0402076",
"authors": [
"D. Simard",
"L. Nadeau",
"H. Kr\u00f6ger"
],
"categories": [
"physics.bio-ph",
"cond-mat.dis-nn"
],
"doi": "10.1016/j.physleta.2004.12.078",
"journal_ref": "Phys. Lett. A336 (2005) 8-15.",
"title": "Fastest learning in small world neural networks",
"url": "https://arxiv.org/abs/physics/0402076"
},
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