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Multiclass GentleAdaboosting

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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.

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

Publisher: Sebastien Paris | License: Freeware | Price: 0.00
Version: 1.0 | Size: 102 KB | Platform: Matlab, Scripts
Released Date: 09-02-2013 | Rating: 0 | Title: Multiclass GentleAdaboosting

Author Url: http://www.mathworks.com
Program Info Url: http://www.mathworks.com
Download Url: http://www.mathworks.com/matlabcentral/fx_files/22997/8/gentleboost.zip

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