Timeline for Do Support Vector Machines have the ability to learn while in use?
Current License: CC BY-SA 4.0
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| when toggle format | what | by | license | comment | |
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| Nov 9, 2022 at 22:11 | history | edited | Faizy | CC BY-SA 4.0 |
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| Nov 9, 2022 at 21:54 | history | edited | Faizy | CC BY-SA 4.0 |
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| Nov 9, 2022 at 14:11 | comment | added | kdbanman | The maximum margin property does not imply online trainability, as this answer claims. What matters is that the maximum margin property is maintained as new data arrives. This depends entirely on the employed optimization algorithm and the data generating process’ properties. (Aside: the kernel trick is only relevant if it affects the interaction of those things.) | |
| Nov 3, 2022 at 3:10 | comment | added | lpounng | Check out this answer. | |
| Nov 3, 2022 at 3:10 | comment | added | lpounng | @Faizy imprecise. The ability to perform online learning is a property of SVM itself; the kernel trick has nothing to do with it. In fact, due to the computational expense of kernel, it is more difficult to learn online with a kernel than without. | |
| Nov 2, 2022 at 21:06 | comment | added | NikoNyrh | How exactly does the "kernel trick" help with online learning? | |
| Oct 21, 2022 at 19:21 | history | answered | Faizy | CC BY-SA 4.0 |