File:Hinge loss vs squared loss.png
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Summary
| Description | English: The hinge loss function is zero on one side, where points are correctly classified, but rises linearly on the other, where they are incorrectly classified. This yields hard-margin SVM behavior when a perfect classifier is possible, and soft-margin behavior in the imperfect case. The squared (L2) loss function penalizes points classified both correctly and incorrectly, and prevents points from being ignored (their contribution to the error is rarely zero). |
| Date | 5 February 2018 () |
| File source | Own Work |
| Author | Alistair Wick |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 23:41, 5 February 2018 | | 570 × 610 (25 KB) | AlistairWick (talk | contribs) | Corrected and clarified loss functions, and added 0-1 loss |
| 21:50, 5 February 2018 |  | 697 × 617 (24 KB) | AlistairWick (talk | contribs) | User created page with UploadWizard |
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