Timeline for Assessing quality of similarity measure
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Jun 15, 2012 at 14:12 | vote | accept | Andreas | ||
| Feb 4, 2012 at 12:43 | answer | added | Has QUIT--Anony-Mousse | timeline score: 2 | |
| Dec 12, 2011 at 17:39 | comment | added | denis | @Andreas, do you want Feature selection / feature elimination ? If so, see RFE in scikit-learn plot_rfe_digits (I recommend linear_model.SGDClassifier). | |
| Dec 12, 2011 at 13:22 | history | edited | Andy W | CC BY-SA 3.0 |
fixed some spelling, tried to format in a more readable way
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| Dec 12, 2011 at 11:23 | history | tweeted | twitter.com/#!/StackStats/status/146188353946140672 | ||
| Dec 12, 2011 at 10:18 | comment | added | Andreas | No i selected differnt similarities that fit the matching idea logically. My problem is to sub-select attributes from the data that fully determine their class (Its a word distribution where only a few words have high probability and the rest i a low probability long tail). Hence i subselect differnt amounts of those words and compare the outcomes of the similarity measure. The target would be an optimal seperation between matches and non matches. In the moment i only look at how the values spread and the optimisation target is to max this spread | |
| Dec 12, 2011 at 10:12 | comment | added | ttnphns | Similarity measure is selected mostly on the basis of theoretical/logical rationale, not empirically. Is it that you fail to work out the rationale that you go for distributional properties in order to select among measures? | |
| Dec 12, 2011 at 9:55 | history | edited | user88 | CC BY-SA 3.0 |
added 7 characters in body; edited tags; edited title
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| Dec 12, 2011 at 9:10 | history | asked | Andreas | CC BY-SA 3.0 |