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- W2783235906 abstract "Support vector Machines are a relatively recent machine learning technique. One of the SVM problems is that SVM is currently considerably slower in test phase caused by the large number of the support vectors, which greatly influences it into the practical use. To address this problem, we proposed a simulated annealing algorithm to reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, such that the selected vectors best approximate the original discriminant function. Experimental results show that the proposed method can reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, confirm the theoretical results and improve classification accuracy." @default.
- W2783235906 created "2018-01-26" @default.
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- W2783235906 date "2017-05-01" @default.
- W2783235906 modified "2023-09-24" @default.
- W2783235906 title "Reducing the Solution of Support Vector Machines Using Simulated Annealing Algorithm" @default.
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- W2783235906 doi "https://doi.org/10.1109/iccairo.2017.30" @default.
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