Timeline for Improving SVM performance on data with missing features and outliers?
Current License: CC BY-SA 3.0
8 events
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Sep 6, 2016 at 12:19 | history | edited | Andre Silva | CC BY-SA 3.0 |
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May 13, 2012 at 19:34 | history | edited | fgregg | CC BY-SA 3.0 |
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May 13, 2012 at 19:33 | comment | added | fgregg | I mean linearly separable in some feature space. | |
May 13, 2012 at 19:30 | comment | added | user603 | fgregg: you mean linearly separable. | |
May 13, 2012 at 17:52 | history | edited | fgregg | CC BY-SA 3.0 |
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May 13, 2012 at 17:51 | comment | added | fgregg | I should have specified that SVM's resistance to outliers only hold if the classes are separable. I'll make the change. | |
May 13, 2012 at 16:18 | comment | added | TenaliRaman | "since one of the properties of SVM is that it effectively ignores all the data that is far from the decision boundary". This statement is incorrect. SVM is actually very sensitive to outliers. There is still ongoing research on how to supress the influence of outliers like this one for example. | |
May 13, 2012 at 14:19 | history | answered | fgregg | CC BY-SA 3.0 |