What are the primary advantages and disadvantages of using Support Vector Machines (SVM) in classification and regression tasks?
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Support Vector Machines (SVM) offer advantages such as robustness in handling large numbers of predictors with limited sample sizes , enhanced generalization ability when incorporating known feature-evolution priors , and good performance in terms of accuracy, sparsity, and flexibility in regression tasks . However, SVMs have limitations like sensitivity to noise and outliers, which can affect the model's boundary . Additionally, traditional SVMs may not fully utilize training data, leading to potential loss of information and local accuracy issues . To address these limitations, researchers have proposed variants and extensions of SVM, such as modified SVR models that extract more information from existing training data to improve performance without requiring additional points .
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| Papers (4) | Insight |
|---|---|
10 Citations | Advantages of Support Vector Regression (SVR): accurate modeling, efficient with high-dimensional data. Disadvantages: parameter selection challenges, limited local accuracy near training points. |
| Advantages of SVM in classification: powerful for analyzing data with many predictors and limited samples. Disadvantages: suboptimal for sparse data and time-to-event outcomes. | |
11 Citations | Advantages of SVMs include effective classification, while disadvantages involve sensitivity to parameter tuning. The paper introduces KFEP-SVMs to enhance generalization ability by capturing feature importance dynamically. |
19 Citations | Advantages of SVM include versatility in classification and regression tasks, while limitations involve sensitivity to noise or outliers. Various robust variants aim to address these challenges. |
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