Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge.
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| Název: | Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge. |
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| Autoři: | Do, Thanh-Nghi |
| Zdroj: | International Journal of Web Information Systems; 2022, Vol. 18 Issue 2/3, p137-155, 19p |
| Abstrakt: | Purpose: This paper aims to propose the new incremental and parallel training algorithm of proximal support vector machines (Inc-Par-PSVM) tailored on the edge device (i.e. the Jetson Nano) to handle the large-scale ImageNet challenging problem. Design/methodology/approach: The Inc-Par-PSVM trains in the incremental and parallel manner ensemble binary PSVM classifiers used for the One-Versus-All multiclass strategy on the Jetson Nano. The binary PSVM model is the average in bagged binary PSVM models built in undersampling training data block. Findings: The empirical test results on the ImageNet data set show that the Inc-Par-PSVM algorithm with the Jetson Nano (Quad-core ARM A57 @ 1.43 GHz, 128-core NVIDIA Maxwell architecture-based graphics processing unit, 4 GB RAM) is faster and more accurate than the state-of-the-art linear SVM algorithm run on a PC [Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32 GB RAM]. Originality/value: The new incremental and parallel PSVM algorithm tailored on the Jetson Nano is able to efficiently handle the large-scale ImageNet challenge with 1.2 million images and 1,000 classes. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Complementary Index |
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