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Manuel Günther authored
Reworked xbob.boosting:\n- Rewrote bindings using Python C-API\n- Improved documentation\n- Implemented more tests\n- Put machines in xbob.boosting.machine sub-module.
Manuel Günther authoredReworked xbob.boosting:\n- Rewrote bindings using Python C-API\n- Improved documentation\n- Implemented more tests\n- Put machines in xbob.boosting.machine sub-module.
Generalized Boosting Framework using Stump and Look Up Table (LUT) based Weak Classifier
The package implements a generalized boosting framework, which incorporates different boosting approaches. 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. |
Installation:
Bob
The boosting framework is dependent on the open source signal-processing and machine learning toolbox Bob, which you need to download from its web page. For more information, please read Bob's installation instructions.
This package
The most simple way to download the latest stable version of the package is to use the Download button above and extract the archive into a directory of your choice. If y want, you can also check out the latest development branch of this package using:
$ git clone https://gitlab.idiap.ch/biometric/xbob-boosting.git
Afterwards, please open a terminal in this directory and call:
$ python bootstrap.py
$ ./bin/buildout
These 2 commands should download and install all dependencies and get you a fully operational test and development environment.