Learning sufficient representation for spatio-temporal deep network using information filter
This article introduced an improved spatio - temporal deep network based on information filter method for learning sufficient representation. The proposed method aims to improve feature learning capability while modeling spatial and temporal dependencies. Experiments on pattern recognition are condu...
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| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| _version_ | 1833411111528955904 |
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| author | Hu Y. Neoh D.T.H. Sahari K.S.M. Loo C.K. |
| author2 | 56096604000 |
| author_facet | 56096604000 Hu Y. Neoh D.T.H. Sahari K.S.M. Loo C.K. |
| author_sort | Hu Y. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This article introduced an improved spatio - temporal deep network based on information filter method for learning sufficient representation. The proposed method aims to improve feature learning capability while modeling spatial and temporal dependencies. Experiments on pattern recognition are conducted to validate the effectiveness of the proposed method. © 2014 IEEE. |
| format | Conference Paper |
| id | my.uniten.dspace-21973 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-219732023-05-16T10:46:23Z Learning sufficient representation for spatio-temporal deep network using information filter Hu Y. Neoh D.T.H. Sahari K.S.M. Loo C.K. 56096604000 56942483000 57218170038 55663408900 This article introduced an improved spatio - temporal deep network based on information filter method for learning sufficient representation. The proposed method aims to improve feature learning capability while modeling spatial and temporal dependencies. Experiments on pattern recognition are conducted to validate the effectiveness of the proposed method. © 2014 IEEE. Final 2023-05-16T02:46:23Z 2023-05-16T02:46:23Z 2014 Conference Paper 10.1109/SII.2014.7028116 2-s2.0-84946193982 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946193982&doi=10.1109%2fSII.2014.7028116&partnerID=40&md5=b2fdd414903d11217d4ef6780ca3cf2b https://irepository.uniten.edu.my/handle/123456789/21973 7028116 655 658 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Hu Y. Neoh D.T.H. Sahari K.S.M. Loo C.K. Learning sufficient representation for spatio-temporal deep network using information filter |
| title | Learning sufficient representation for spatio-temporal deep network using information filter |
| title_full | Learning sufficient representation for spatio-temporal deep network using information filter |
| title_fullStr | Learning sufficient representation for spatio-temporal deep network using information filter |
| title_full_unstemmed | Learning sufficient representation for spatio-temporal deep network using information filter |
| title_short | Learning sufficient representation for spatio-temporal deep network using information filter |
| title_sort | learning sufficient representation for spatio-temporal deep network using information filter |
| url_provider | http://dspace.uniten.edu.my/ |