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  • License: Freeware
  • Last update: 6 years ago
  • Total downloads: 200
  • Price: Free |
  • Operating system: Linux, Mac OS X, Win All, BSD, Solaris
  • Publisher: Sebastien Paris (27 other programs)
See full specifications

scripts default iconMulticlass GentleAdaboosting (Scripts) Publisher's description

A fast Gentle Adaboost classifier with two different weak-learners: i) decision stump and ii) perceptron.

A fast Gentle Adaboost classifier with two different weak-learners: i) decision stump and ii) perceptron. Multiclass is performed with the one-against-all strategy.


model = gentleboost_model(X , y , [T] , [options]);


X Features matrix (d x N)
y Labels (1 x N). If y represent binary labels vector then y_i={-1,1}, i=1,...,N
T Number of weak learners (default T = 100)
weaklearner Choice of weaklearner 0 : Decision Stump, 1 : Perceptron (default weaklearner = 0)

epsi Epsilon constant in the sigmoid function used in the perceptron (default epsi = 1)
lambda Regularization parameter for the perceptron's weights update (default lambda = 1e-3)
max_ite Maximum number of iterations of the perceptron algorithm (default max_ite = 100)


model Structure of model output

featureIdx Features index of the T best weaklearners (T x m) where m is the number of class.
For binary classification m is force to 1.
th Theta (T x m)
b Bias parameter (T x m)
a Decision Stump extra parameter (T x m)

Please run mexme_gentleboost to compile mex-files on your platform.

Please run test_gentleboost_model to run the demo.

N.B. Last build of libsvm is also included and slightly modified to suppress verbose.

System Requirements:

A C/C++ compiler
Program Release Status: Major Update
Program Install Support: Install and Uninstall

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