Sequence recognition with spatio-temporal long-term memory organization
In this work, we propose a connectionist memory structure for spatio-temporal sequence learning and recognition inspired by the Long-Term Memory structure of human cortex. Besides symbolic data, our framework is able to continuously process real-valued multi-dimensional data stream. This capability...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98284 http://hdl.handle.net/10220/12399 |
| _version_ | 1826114721281998848 |
|---|---|
| author | Nguyen, Vu Anh Goh, Wooi Boon Starzyk, Janusz A. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Nguyen, Vu Anh Goh, Wooi Boon Starzyk, Janusz A. |
| author_sort | Nguyen, Vu Anh |
| collection | NTU |
| description | In this work, we propose a connectionist memory structure for spatio-temporal sequence learning and recognition inspired by the Long-Term Memory structure of human cortex. Besides symbolic data, our framework is able to continuously process real-valued multi-dimensional data stream. This capability is made possible by addressing three critical problems in spatio-temporal learning, namely error tolerance, significance of sequence's elements and memory forgetting mechanism. We demonstrate the potential of the framework with a synthetic example and a real world example, namely the task of hand-sign language interpretation with the Australian Sign Language dataset. |
| first_indexed | 2024-10-01T03:43:46Z |
| format | Conference Paper |
| id | ntu-10356/98284 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:43:46Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/982842020-05-28T07:19:04Z Sequence recognition with spatio-temporal long-term memory organization Nguyen, Vu Anh Goh, Wooi Boon Starzyk, Janusz A. School of Computer Engineering International Joint Conference on Neural Networks (2012 : Brisbane, Australia) DRNTU::Engineering::Computer science and engineering In this work, we propose a connectionist memory structure for spatio-temporal sequence learning and recognition inspired by the Long-Term Memory structure of human cortex. Besides symbolic data, our framework is able to continuously process real-valued multi-dimensional data stream. This capability is made possible by addressing three critical problems in spatio-temporal learning, namely error tolerance, significance of sequence's elements and memory forgetting mechanism. We demonstrate the potential of the framework with a synthetic example and a real world example, namely the task of hand-sign language interpretation with the Australian Sign Language dataset. 2013-07-26T06:53:20Z 2019-12-06T19:53:10Z 2013-07-26T06:53:20Z 2019-12-06T19:53:10Z 2012 2012 Conference Paper Nguyen, V. A., Starzyk, J. A., & Goh, W. B. (2012). Sequence recognition with spatio-temporal long-term memory organization. The 2012 International Joint Conference on Neural Networks (IJCNN). https://hdl.handle.net/10356/98284 http://hdl.handle.net/10220/12399 10.1109/IJCNN.2012.6252682 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Nguyen, Vu Anh Goh, Wooi Boon Starzyk, Janusz A. Sequence recognition with spatio-temporal long-term memory organization |
| title | Sequence recognition with spatio-temporal long-term memory organization |
| title_full | Sequence recognition with spatio-temporal long-term memory organization |
| title_fullStr | Sequence recognition with spatio-temporal long-term memory organization |
| title_full_unstemmed | Sequence recognition with spatio-temporal long-term memory organization |
| title_short | Sequence recognition with spatio-temporal long-term memory organization |
| title_sort | sequence recognition with spatio temporal long term memory organization |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/98284 http://hdl.handle.net/10220/12399 |
| work_keys_str_mv | AT nguyenvuanh sequencerecognitionwithspatiotemporallongtermmemoryorganization AT gohwooiboon sequencerecognitionwithspatiotemporallongtermmemoryorganization AT starzykjanusza sequencerecognitionwithspatiotemporallongtermmemoryorganization |