Generalized Boosting Framework using Stump and Look Up Table (LUT) based Weak Classifiers¶
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Improve documentation.
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The package implements a generalized boosting framework, which incorporates different boosting approaches. The implementation is a mix of pure Python code and C++ implementations of identified bottle-necks, including their python bindings.
The Boosting algorithms implemented in this package are:
- Gradient Boost [Fri00] (generalized version of Adaboost [FS99]) for univariate cases using stump decision classifiers, as in [VJ04].
- TaylorBoost [SMV11] for univariate and multivariate cases using Look-Up-Table based classifiers [Ata12]
| [Fri00] | Jerome H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29:1189–1232, 2000. |
| [FS99] | Yoav Freund and Robert E. Schapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September, 1999. |
| [VJ04] | Paul Viola and Michael J. Jones. Robust real-time face detection. International Journal of Computer Vision (IJCV), 57(2): 137–154, 2004. |
| [SMV11] | Mohammad J. Saberian, Hamed Masnadi-Shirazi, Nuno Vasconcelos. TaylorBoost: First and second-order boosting algorithms with explicit margin control. IEEE Conference on Conference on Computer Vision and Pattern Recognition (CVPR), 2929–2934, 2011. |
| [Ata12] | Cosmin Atanasoaei. Multivariate boosting with look-up tables for face processing. PhD Thesis, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, 2012. |