A hybrid spiking neural network model for multivariate data classification and visualization.
This study proposes a hybrid model of Self-Organizing Map with modified adaptive coordinates (SOM-AC) and Spiking Neural Network (SNN) for multivariate spatial and temporal data visualization and classification. SOM is one of the most prominent unsupervised learning algorithms. Recently, many extens...
| Main Authors: | , , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/9976/ http://ir.unimas.my/id/eprint/9976/1/A%20hybrid.pdf |
| _version_ | 1848836684912787456 |
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| author | Ming, Leong Yii Teh, Chee Siong Chen, Chwen Jen |
| author_facet | Ming, Leong Yii Teh, Chee Siong Chen, Chwen Jen |
| author_sort | Ming, Leong Yii |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | This study proposes a hybrid model of Self-Organizing Map with modified adaptive coordinates (SOM-AC) and Spiking Neural Network (SNN) for multivariate spatial and temporal data visualization and classification. SOM is one of the most prominent unsupervised learning algorithms. Recently, many extensions for SOM have been proposed for temporal processing. However, none of the extensions uses spikes as means of information processing. SNN has potential for qualitative advancements in both biological relevancy and computational power. Therefore, this hybrid learning model is proposed to harness the advantages of both SOM-AC and SNN to produce intuitive multivariate data classification and visualization. Empirical studies of the hybrid model using synthetic and benchmarking datasets yielded promising classification accuracy and intuitive rich visualization. |
| first_indexed | 2025-11-15T06:27:41Z |
| format | Proceeding |
| id | unimas-9976 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:27:41Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-99762022-08-23T03:17:53Z http://ir.unimas.my/id/eprint/9976/ A hybrid spiking neural network model for multivariate data classification and visualization. Ming, Leong Yii Teh, Chee Siong Chen, Chwen Jen L Education (General) T Technology (General) This study proposes a hybrid model of Self-Organizing Map with modified adaptive coordinates (SOM-AC) and Spiking Neural Network (SNN) for multivariate spatial and temporal data visualization and classification. SOM is one of the most prominent unsupervised learning algorithms. Recently, many extensions for SOM have been proposed for temporal processing. However, none of the extensions uses spikes as means of information processing. SNN has potential for qualitative advancements in both biological relevancy and computational power. Therefore, this hybrid learning model is proposed to harness the advantages of both SOM-AC and SNN to produce intuitive multivariate data classification and visualization. Empirical studies of the hybrid model using synthetic and benchmarking datasets yielded promising classification accuracy and intuitive rich visualization. 2011 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/9976/1/A%20hybrid.pdf Ming, Leong Yii and Teh, Chee Siong and Chen, Chwen Jen (2011) A hybrid spiking neural network model for multivariate data classification and visualization. In: 2011 7th International Conference on Information Technology in Asia, 12-13 July 2011, Sarawak,. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5999509&tag=1 |
| spellingShingle | L Education (General) T Technology (General) Ming, Leong Yii Teh, Chee Siong Chen, Chwen Jen A hybrid spiking neural network model for multivariate data classification and visualization. |
| title | A hybrid spiking neural network model for multivariate data classification and visualization. |
| title_full | A hybrid spiking neural network model for multivariate data classification and visualization. |
| title_fullStr | A hybrid spiking neural network model for multivariate data classification and visualization. |
| title_full_unstemmed | A hybrid spiking neural network model for multivariate data classification and visualization. |
| title_short | A hybrid spiking neural network model for multivariate data classification and visualization. |
| title_sort | hybrid spiking neural network model for multivariate data classification and visualization. |
| topic | L Education (General) T Technology (General) |
| url | http://ir.unimas.my/id/eprint/9976/ http://ir.unimas.my/id/eprint/9976/ http://ir.unimas.my/id/eprint/9976/1/A%20hybrid.pdf |