Timeline for Using k-means for reducing the size of the training set of a Kernel SVM
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| May 13, 2015 at 7:14 | vote | accept | Marius Ion | ||
| May 8, 2015 at 17:54 | history | edited | Jonathan Lisic | CC BY-SA 3.0 |
clarity
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| May 8, 2015 at 16:22 | history | edited | Jonathan Lisic | CC BY-SA 3.0 |
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| May 8, 2015 at 16:11 | history | edited | Jonathan Lisic | CC BY-SA 3.0 |
added 480 characters in body
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| May 8, 2015 at 16:03 | comment | added | meh | To amplify on the answer above, if you run a random Forest, you can (with most software packages) extract variable importance. That my solve your variable reduction problem. | |
| May 8, 2015 at 15:30 | history | answered | Jonathan Lisic | CC BY-SA 3.0 |