Timeline for Deep learning vs. Decision trees and boosting methods
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 4, 2014 at 14:40 | vote | accept | Amelio Vazquez-Reina | ||
| Jul 7, 2011 at 15:28 | comment | added | user5268 | Generally in terms of image processing, DL methods will act as feature extractors which can then be paired with SVMs to do classification. These methods are generally comparable to hand-engineered approaches like SIFT, SURF, and HOG. DL methods have been extended to video with gated CRBMs, and ISA. Hand-engineered methods include HOG/HOF, HOG3d, and eSURF (see Wang et al. 2009 for a good comparison). | |
| Jul 7, 2011 at 13:21 | comment | added | Amelio Vazquez-Reina | Thanks @f(x), I'm interested in the classification of (2D or 3D) pixel segments or patches, but I wanted to keep the original question as general as possible. If different methods work best on different types of datasets, I would be interested in a discussion addressing these differences. | |
| Jul 6, 2011 at 20:16 | history | answered | user5268 | CC BY-SA 3.0 |