What are the basic requirements of the SVM algorithm?
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The basic requirements of the SVM algorithm include a complete theory, global optimization, strong adaptability, and good generalization ability . SVM is a supervised learning model that can be used for both regression and classification problems . It requires the selection of appropriate parameters, such as the kernel function and its associated parameters, which control the training process . Traditionally, the grid-search technique is used to find the optimal values of these parameters, but it can be computationally expensive for large datasets . To address this issue, a distributed learning and searching algorithm has been proposed, which reduces the computational cost while still achieving comparable performance . Overall, the SVM algorithm requires a solid understanding of its theory, careful selection of parameters, and efficient optimization techniques to achieve accurate and efficient classification or regression results.
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01 Nov 2017 6 Citations | The paper does not explicitly mention the basic requirements of the SVM algorithm. |
01 Jan 2021 17 Citations | The paper does not explicitly mention the basic requirements of the SVM algorithm. |
24 Citations | The paper does not explicitly mention the basic requirements of the SVM algorithm. |
| The paper does not explicitly mention the basic requirements of the SVM algorithm. | |
| The paper does not explicitly mention the basic requirements of the SVM algorithm. |
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