Interleaved incremental/decremental support vector machine for embedded system
Incremental and Decremental Support Vector Machine (IDSVM) is a widely used incremental learning algorithm that is highly accurate but requires high computational complexity. For IDSVM to be deployed in embedded systems, moving window architecture is needed to limit the number of support vectors in...
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my.utm.971432022-09-23T01:43:31Z http://eprints.utm.my/id/eprint/97143/ Interleaved incremental/decremental support vector machine for embedded system Sirkunan, Jeevan Shaikh-Husin, N. Marsono, M. N. TK Electrical engineering. Electronics Nuclear engineering Incremental and Decremental Support Vector Machine (IDSVM) is a widely used incremental learning algorithm that is highly accurate but requires high computational complexity. For IDSVM to be deployed in embedded systems, moving window architecture is needed to limit the number of support vectors in the model. This increases the complexity of the system as data need to be unlearned while learning new data. This work proposes an interleaved IDSVM (IIDSVM) architecture that performs incremental and decremental learning simultaneously. This work targets embedded system platform with limited on-chip memory. The proposed solution is able to get an improvement of 60%-70% in terms of speed while producing similar accuracy with IDSVM. 2019 Conference or Workshop Item PeerReviewed Sirkunan, Jeevan and Shaikh-Husin, N. and Marsono, M. N. (2019) Interleaved incremental/decremental support vector machine for embedded system. In: 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019, 26 - 29 May 2019, Sapporo, Japan. http://dx.doi.org/10.1109/ISCAS.2019.8702745 |
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TK Electrical engineering. Electronics Nuclear engineering Sirkunan, Jeevan Shaikh-Husin, N. Marsono, M. N. Interleaved incremental/decremental support vector machine for embedded system |
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Incremental and Decremental Support Vector Machine (IDSVM) is a widely used incremental learning algorithm that is highly accurate but requires high computational complexity. For IDSVM to be deployed in embedded systems, moving window architecture is needed to limit the number of support vectors in the model. This increases the complexity of the system as data need to be unlearned while learning new data. This work proposes an interleaved IDSVM (IIDSVM) architecture that performs incremental and decremental learning simultaneously. This work targets embedded system platform with limited on-chip memory. The proposed solution is able to get an improvement of 60%-70% in terms of speed while producing similar accuracy with IDSVM. |
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Conference or Workshop Item |
author |
Sirkunan, Jeevan Shaikh-Husin, N. Marsono, M. N. |
author_facet |
Sirkunan, Jeevan Shaikh-Husin, N. Marsono, M. N. |
author_sort |
Sirkunan, Jeevan |
title |
Interleaved incremental/decremental support vector machine for embedded system |
title_short |
Interleaved incremental/decremental support vector machine for embedded system |
title_full |
Interleaved incremental/decremental support vector machine for embedded system |
title_fullStr |
Interleaved incremental/decremental support vector machine for embedded system |
title_full_unstemmed |
Interleaved incremental/decremental support vector machine for embedded system |
title_sort |
interleaved incremental/decremental support vector machine for embedded system |
publishDate |
2019 |
url |
http://eprints.utm.my/id/eprint/97143/ http://dx.doi.org/10.1109/ISCAS.2019.8702745 |
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1745562343917486080 |
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13.15806 |