Timeline for Calculating the value of $b^{*}$ in an SVM
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 9, 2020 at 11:08 | history | bounty ended | Gerard | ||
| Mar 9, 2020 at 11:08 | vote | accept | Gerard | ||
| Mar 7, 2020 at 10:05 | history | edited | jpmuc | CC BY-SA 4.0 |
improved the answer
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| Mar 6, 2020 at 13:05 | history | edited | jpmuc | CC BY-SA 4.0 |
elaborate answer for the soft margin case
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| Mar 6, 2020 at 13:00 | comment | added | jpmuc | added the soft case in the answer | |
| Mar 6, 2020 at 13:00 | history | edited | jpmuc | CC BY-SA 4.0 |
elaborate answer for the soft margin case
|
| Mar 5, 2020 at 15:31 | comment | added | Gerard | Could you elaborate a bit on that with maybe some hints on how to deal with the soft margin case? | |
| Mar 3, 2020 at 17:55 | comment | added | jpmuc | These equalities corresponds to the active constraints (see the section on the KKT conditions), that is, the vectors lying exactly on the separating hyperplane. | |
| Mar 3, 2020 at 16:54 | comment | added | Gerard | "The closest positive and negative examples to the separating hyperplane..." - how does this follow from the mathematics? That is to say, how does the formulation of the optimization problem imply that the equalities you mentioned do, in fact, hold. Ideally, once the optimization problem has been formulated we should not need to refer to the SVM setting at all. The reason I want a mathematical derivation is that your argument does not help in finding $b*$ for the soft margin case | |
| Mar 3, 2020 at 8:21 | history | answered | jpmuc | CC BY-SA 4.0 |