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

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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:

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.

Documentation

• Boosting Strong Classifiers
  • Weak Classifiers
  • Strong classifier
  • Loss functions
• Example: Handwritten Digit Classification
  • Running the example script
  • One exemplary test case in details
• Python API
  • Machines
  • Trainers
  • Loss functions
  • Details

Indices and tables

• Index
• Module Index
• Search Page