Timeline for SVM: Why does the number of support vectors decrease when C is increased?
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Apr 1, 2017 at 23:54 | vote | accept | kingledion | ||
| Mar 28, 2017 at 13:47 | history | edited | Dikran Marsupial | CC BY-SA 3.0 |
added 29 characters in body
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| Mar 28, 2017 at 13:45 | comment | added | Dikran Marsupial | @kingledion the number of support vectors is not a particularly good measure of complexity as the magnitude of the weights is also relevant. Vapnik defines the concept of an essential support vector (one that the decision boundary cannot be defined without), but can often find a good approximation with fewer SVs than the algorithm gives. Note also that increasing the complexity of a hypothesis class does not mean increasing the complexity of a particular hypothesis from that class. | |
| Mar 28, 2017 at 13:42 | comment | added | Dikran Marsupial | @DaneelOlivaw I just mean the output of the support vector machine (before thresholding). | |
| Mar 28, 2017 at 13:24 | comment | added | kingledion | With respect to this answer of yours, you say there that increasing C increases the complexity of the hypothesis class. Can you explain in your answer how the hypothesis class can get more complex if the number of support vectors is reduced? I do not understand that. | |
| Mar 28, 2017 at 12:59 | comment | added | Daneel Olivaw | Could you clarify what do you mean by "kernel expansion" please @Dikran Marsupial? | |
| Mar 28, 2017 at 12:53 | history | answered | Dikran Marsupial | CC BY-SA 3.0 |