Timeline for Visualizing SVM results
Current License: CC BY-SA 3.0
3 events
| when toggle format | what | by | license | comment | |
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| Jun 2, 2015 at 15:12 | comment | added | KalEl | As long as the model learned in the full representation of the data, the reduced 2-dimensional view is simply a view - visualizing what happened. In other cases though, often where you have high dimensional feature-space, you do apply your models on the reduced space - there you do change the problem to a 'new' one. This is frequently done in text mining to reduce the term-document-matrix before any model is fitted. | |
| Jun 2, 2015 at 15:06 | comment | added | Ruthger Righart | Thank you very much for this thorough answer! I read about using PCA to do this. I wonder if the obtained hyperplane with the resulting two components reliably represents the one of the original dimensions, or if it is simply a new data problem that one creates by reducing dimensions. Any ideas on this? | |
| Jun 2, 2015 at 14:44 | history | answered | KalEl | CC BY-SA 3.0 |