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- function model = banditron_multi_train(X,Y,model)
- % BANDITRON_MULTI_TRAIN Banditron algorithm
- %
- % MODEL = BANDITRON_MULTI_TRAIN(X,Y,MODEL) trains a multiclass
- % classifier according to the Banditron algorithm.
- %
- % Additional parameters:
- % - model.n_cla is the number of classes.
- % - model.gamma is the parameter that controls the trade-off between
- % exploration and exploitation.
- % Default value is 0.01.
- %
- % References:
- % - Kakade, S. M., Shalev-Shwartz, S., & Tewari, A. (2008).
- % Efficient bandit algorithms for online multiclass prediction.
- % Proceedings of the 25th International Conference on Machine
- % Learning (pp. 440–447).
-
- % This file is part of the DOGMA library for MATLAB.
- % Copyright (C) 2009-2011, Francesco Orabona
- %
- % This program is free software: you can redistribute it and/or modify
- % it under the terms of the GNU General Public License as published by
- % the Free Software Foundation, either version 3 of the License, or
- % (at your option) any later version.
- %
- % This program is distributed in the hope that it will be useful,
- % but WITHOUT ANY WARRANTY; without even the implied warranty of
- % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- % GNU General Public License for more details.
- %
- % You should have received a copy of the GNU General Public License
- % along with this program. If not, see <http://www.gnu.org/licenses/>.
- %
- % Contact the author: francesco [at] orabona.com
-
- n = length(Y); % number of training samples
-
- if isfield(model,'iter')==0
- model.iter = 0;
- model.w = zeros(model.n_cla,size(X,1));
- model.errTot = 0;
- model.numSV = zeros(numel(Y),1);
- model.aer = zeros(numel(Y),1);
- model.pred = zeros(model.n_cla,numel(Y));
- end
-
- if isfield(model,'gamma')==0
- model.gamma = .01;
- end
-
- for i=1:n
- model.iter = model.iter+1;
-
- val_f = model.w*X(:,i);
-
- Yi = Y(i);
-
- [mx_f,y_hat] = max(val_f);
- Prob = zeros(1,model.n_cla)+model.gamma/model.n_cla;
- Prob(y_hat) = Prob(y_hat)+1-model.gamma;
- random_vect = (rand<cumsum(Prob));
- [dummy,y_tilde] = max(random_vect);
-
- model.errTot = model.errTot+(y_tilde~=Yi);
- model.aer(model.iter) = model.errTot/model.iter;
- model.pred(:,model.iter) = val_f;
-
- model.w(y_hat,:) = model.w(y_hat,:)-X(:,i)';
-
- if y_tilde==Yi
- model.w(Yi,:) = model.w(Yi,:)+1/Prob(y_tilde)*X(:,i)';
- end
-
- model.numSV(model.iter) = numel(model.S);
-
- if mod(i,model.step)==0
- fprintf('#%.0f AER:%5.2f\n', ...
- ceil(i/1000),model.aer(model.iter)*100);
- end
- end
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