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In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables, allowing learning of non-linear models.
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kernel polynomial feature features space vectors training samples input regression similarity combinations displaystyle commonly support polynomials non-linear given conjunctions kernelized
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Polynomial kernel | is a | kernel function commonly used with support vector machines | 0.90 | text |
| Polynomial kernel | related to Definition | For | 0.60 | section |
| Polynomial kernel | related to Definition | When | 0.60 | section |
| Polynomial kernel | related to Practical use | Although | 0.60 | section |
| Polynomial kernel | related to Practical use | RBF | 0.60 | section |
| Polynomial kernel | related to Practical use | SVM | 0.60 | section |
| Polynomial kernel | related to Practical use | NLP | 0.60 | section |
| Polynomial kernel | related to Practical use | The | 0.60 | section |
| Polynomial kernel | related to Practical use | Various | 0.60 | section |
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