A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition
Hierarchical Concatenation Networks (HCN) are inspired by the way humans recognize patterns; i.e. by concatenating small features. In HCNs patterns are split into small parts, and then concatenated and activated in the network’s layers. The research in this thesis investigated and explored feature e...
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| Format: | Thesis |
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Curtin University
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/73517 |
| _version_ | 1848763035488878592 |
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| author | Ramli, Irwan |
| author_facet | Ramli, Irwan |
| author_sort | Ramli, Irwan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Hierarchical Concatenation Networks (HCN) are inspired by the way humans recognize patterns; i.e. by concatenating small features. In HCNs patterns are split into small parts, and then concatenated and activated in the network’s layers. The research in this thesis investigated and explored feature extraction methods, similarity measures, and classification using HCNs. Results indicate that HCNs can be used in automatic pattern recognition systems with better performance rate on the lower layer than the top layer. |
| first_indexed | 2025-11-14T10:57:04Z |
| format | Thesis |
| id | curtin-20.500.11937-73517 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:57:04Z |
| publishDate | 2018 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-735172019-01-07T05:46:41Z A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition Ramli, Irwan Hierarchical Concatenation Networks (HCN) are inspired by the way humans recognize patterns; i.e. by concatenating small features. In HCNs patterns are split into small parts, and then concatenated and activated in the network’s layers. The research in this thesis investigated and explored feature extraction methods, similarity measures, and classification using HCNs. Results indicate that HCNs can be used in automatic pattern recognition systems with better performance rate on the lower layer than the top layer. 2018 Thesis http://hdl.handle.net/20.500.11937/73517 Curtin University fulltext |
| spellingShingle | Ramli, Irwan A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition |
| title | A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition |
| title_full | A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition |
| title_fullStr | A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition |
| title_full_unstemmed | A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition |
| title_short | A Study of Hierarchical Concatenation Networks in the Area of Pattern Recognition |
| title_sort | study of hierarchical concatenation networks in the area of pattern recognition |
| url | http://hdl.handle.net/20.500.11937/73517 |