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https://hdl.handle.net/10356/4543
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kyaw, Zin Min. | en_US |
dc.date.accessioned | 2008-09-17T09:53:51Z | - |
dc.date.available | 2008-09-17T09:53:51Z | - |
dc.date.copyright | 2003 | en_US |
dc.date.issued | 2003 | - |
dc.identifier.uri | http://hdl.handle.net/10356/4543 | - |
dc.description.abstract | This report documents the underlining theories and neural network that lead to the development of handwritten digit recognition architecture that are capable of recognizing handwritten digit with recognition accuracy of up to 76%. | en_US |
dc.rights | Nanyang Technological University | en_US |
dc.subject | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems | - |
dc.title | Multi-layer neural networks for handwritten digit recognition | en_US |
dc.type | Thesis | en_US |
dc.contributor.supervisor | Saratchandran, Paramasivan | en_US |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.description.degree | Master of Science (Computer Control and Automation) | en_US |
item.grantfulltext | restricted | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | EEE Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
EEE-THESES_567.pdf Restricted Access | 1.11 MB | Adobe PDF | View/Open |
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