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Multiclass GentleAdaboosting 1.0

  Date Added: July 06, 2013  |  Visits: 302

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.Usage ------ model = gentleboost_model(X , y , [T] , [options]); Inputs ------- 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) options 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) Outputs ------- 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.

Requirements: No special requirements
Platforms: Matlab
Keyword: Adaboost Classifier Algorithm Default And Ii Bias Parameter Binary Classification Binary Labels Build Choice Classfor Binary Classifier Compile Mexfiles Constant Decision Stump Default Epsi Gentleboost Included Slightly Weaklearners Weig
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License: Freeware Size: 102.4 KB
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