Support Vector Machines: Soft Margins, Duality, and Kernels
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Adapted from an appendix of my MS thesis. Further Reading The traditional derivation of the margin is known as the hard margin SVM. The reason for the expression “hard” is because the formulation does not all for any violations of the margin condition. In the case where data is not linearly separable, we may wish to allow some examples to fall within the margin region, or even to be on the wrong side of the hyperplane. The model that allows for some classification errors is called the soft…
1Key Takeaways
- Adapted from an appendix of my MS thesis.
- Further Reading The traditional derivation of the margin is known as the hard margin SVM.
- The reason for the expression “hard” is because the formulation does not all for any violations of the margin condition.
- In the case where data is not linearly separable, we may wish to allow some examples to fall within the margin region, or even to be on the wrong side of the hyperplane.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that adapted from an appendix of my MS thesis.
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