Does support vector regression (SVR) have computational complexity issues?
Best insight from top research papers
Support vector regression (SVR) has been proven to have excellent generalization capability and computational efficiency, regardless of the dimensionality of the input space . However, when dealing with data streams, SVR cannot be used as it assumes all required data is available at the time of construction. To address this limitation, an incremental regression algorithm called online robust support vector regression (ORSVR) has been proposed. ORSVR efficiently solves regression problems in data streams, even with noise, and speeds up the learning process . Additionally, the computational complexity of SVR can be further reduced by using kernel-free quadratic surface support vector regression (QSSVR) models based on optimal margin distribution (OMD) . These models minimize the variance of functional margins to achieve better generalization capability .
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| Papers (5) | Insight |
|---|---|
32 Citations | Yes, the paper mentions that the proposed approach using K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA) successfully reduces the computation complexity of support vector regression (SVR). |
| The paper does not mention any computational complexity issues with support vector regression (SVR). | |
2 Citations | The paper does not explicitly mention the computational complexity of support vector regression (SVR). |
22 Citations | Yes, support vector regression (SVR) has computational complexity issues. |
3 Citations | The paper does not explicitly mention the computational complexity issues of support vector regression (SVR). |
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