Generalized Boosting Framework using Stump and Look Up Table (LUT) based Weak Classifiers

Todo

Improve documentation.

(The original entry is located in docstring of bob.learn.boosting.BoostedMachine, line 3.)

Todo

Improve documentation.

(The original entry is located in docstring of bob.learn.boosting.LUTMachine, line 3.)

Todo

Improve documentation.

(The original entry is located in docstring of bob.learn.boosting.LUTTrainer, line 3.)

Todo

Write documentation for this

(The original entry is located in docstring of bob.learn.boosting.LUTTrainer.train, line 3.)

Todo

Improve documentation.

(The original entry is located in docstring of bob.learn.boosting.StumpMachine, line 4.)

Todo

write more detailed documentation

(The original entry is located in docstring of bob.learn.boosting.StumpMachine.forward, line 8.)

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:

  1. Gradient Boost [Fri00] (generalized version of Adaboost [FS99]) for univariate cases using stump decision classifiers, as in [VJ04].
  2. 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.

Indices and tables