What are the potential limitations of Support Vector Machines (SVM) in predictive modeling?
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Support Vector Machines (SVM) have limitations in predictive modeling. One limitation is the challenge of handling missing data in electronic health records, which restricts the solidity of findings. Several methods have been developed to handle this issue, such as imputation or complete case analysis, but their limitations persist. However, recent studies have shown that leveraging partially available privileged information can improve model performance, including in SVM. Another limitation is the computational complexity of nonlinear SVM solvers, which increases with the number of samples in training data and requires tuning of extra kernel parameters. To address these challenges, adaptive multilevel learning frameworks have been proposed, which reduce running time and improve classification quality. Additionally, SVM algorithms may yield erroneous classification results when features are correlated with complex network structures. To address this, new learning algorithms have been proposed that accommodate network structures in the features of the instances.
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| The provided paper does not mention the potential limitations of Support Vector Machines (SVM) in predictive modeling. | |
Open access•Proceedings Article 10 Jul 2019 | The paper does not explicitly mention the potential limitations of Support Vector Machines (SVM) in predictive modeling. |
Open access•Posted Content | The paper does not explicitly mention the potential limitations of Support Vector Machines (SVM) in predictive modeling. |
10 Dec 2020 | The paper does not explicitly mention the potential limitations of Support Vector Machines (SVM) in predictive modeling. |
| The provided paper does not mention the potential limitations of Support Vector Machines (SVM) in predictive modeling. |
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