How do I save a python model in SVM?
Answers from top 15 papers
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| Papers (15) | Insight |
|---|---|
| The results show that the proposed prediction model surpasses the traditional SVM in prediction performance. | |
01 Jul 2013 75 Citations | This infers that the SVM model outperforms all other models. |
47 Citations | The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters. |
| We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. | |
18 Feb 2019 | In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data. |
02 Oct 2009 40 Citations | The selection of SVM parameters has an important influence on the classification accuracy of SVM. |
24 Jul 2006 | It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate |
43 Citations | Moreover, this paper proposed an enhancing training method to guarantee the accuracy of SVM model. |
03 Oct 2006 | Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model. |
72 Citations | The results showed that SVM is superior to various other learning techniques considering the generalization capability of produced model. |
75 Citations | The results of model testing showed that the SVM achieves good predictive performance. |
| Results show that both model parameters and training sample size can influence the prediction accuracy of the SVM model. | |
01 Sep 2001 92 Citations | It is shown that the proposed method achieves both significantly higher prediction performance and faster convergence speed in comparison with a single SVM model. |
01 Feb 2016 | The result shows that the SVM model has an optimal parameter on C parameters 0.1 and 0 Epsilon. |
06 Jun 2004 | Numerical results show that the optimal hybrid model outperforms the direct application of SVM by 12.7 percent. |
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