Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/129558| DC Field | Value | Language |
|---|---|---|
| dc.contributor | 應數系 | |
| dc.creator | 班榮超 | |
| dc.creator | Ban, Jung-Chao | |
| dc.creator | Chang, Chih-Hung | |
| dc.date | 2013-11 | |
| dc.date.accessioned | 2020-04-28T05:55:07Z | - |
| dc.date.available | 2020-04-28T05:55:07Z | - |
| dc.date.issued | 2020-04-28T05:55:07Z | - |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/129558 | - |
| dc.description.abstract | This elucidation investigates the Hausdorff dimension of the output space of multi-layer neural networks. When the factor map from the covering space of the output space to the output space has a synchronizing word, the Hausdorff dimension of the output space relates to its topological entropy. This clarifies the geometrical structure of the output space in more details. | |
| dc.format.extent | 208238 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation | Advances in Pure Mathematics, 2013, 3, 9-14 | |
| dc.subject | Multi-Layer Neural Networks ; Hausdorff Dimension ; Sofic Shift ; Output Space | |
| dc.title | Hausdorff Dimension of Multi-Layer Neural Networks | |
| dc.type | article | |
| dc.identifier.doi | 10.4236/apm.2013.39A1002 | |
| dc.doi.uri | https://doi.org/10.4236/apm.2013.39A1002 | |
| item.fulltext | With Fulltext | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| item.cerifentitytype | Publications | - |
| item.grantfulltext | restricted | - |
| item.openairetype | article | - |
| Appears in Collections: | 期刊論文 | |
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