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- function model = k_sel_ada_perc_train(X,Y,model)
- % K_SEL_ADA_PERC_TRAIN Kernel Selective Perceptron algorithm, with adaptive
- % parameter
- %
- % MODEL = K_SEL_ADA_PERC_TRAIN(X,Y,MODEL) trains an classifier
- % according to the Selective Perceptron algorithm, using kernels.
- %
- % Additional parameters:
- % - model.bs governs the sampling rate of the algorithm.
- % Default value is 1.
- %
- % References:
- % - Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006).
- % Worst-Case Analysis of Selective Sampling for Linear Classification
- % Journal of Machine Learning Research, 7, (pp. 1205-1230).
-
- % 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.beta=[];
- model.beta2=[];
- model.errTot=0;
- model.numSV=zeros(numel(Y),1);
- model.aer=zeros(numel(Y),1);
- model.pred=zeros(numel(Y),1);
-
- model.numQueries=0;
- model.maxR2=0;
- end
-
- if isfield(model,'bs')==0
- model.bs=1;
- end
-
- for i=1:n
- model.iter=model.iter+1;
-
- if numel(model.S)>0
- if isempty(model.ker)
- K_f=X(model.S,i);
- else
- K_f=feval(model.ker,model.SV,X(:,i),model.kerparam);
- end
- val_f=model.beta*K_f;
- else
- val_f=0;
- end
-
- Yi=Y(i);
-
- model.errTot=model.errTot+(sign(val_f)~=Yi);
- model.aer(model.iter)=model.errTot/model.iter;
-
- model.pred(model.iter)=val_f;
-
- R2=max(feval(model.ker,X(:,i),X(:,i),model.kerparam),model.maxR2);
- b=model.bs*R2*sqrt(1+size(model.SV,2));
- Z=(rand<b/(abs(val_f)+b));
-
- model.numQueries=model.numQueries+Z;
-
- if Z==1 && Yi*val_f<=0
- model.beta(end+1)=Yi;
- model.S(end+1)=model.iter;
- if ~isempty(model.ker)
- model.SV(:,end+1)=X(:,i);
- end
- model.maxR2=R2;
-
- model.beta2(end+1)=0;
- end
-
- model.beta2=model.beta2+model.beta;
-
- model.numSV(model.iter)=numel(model.S);
-
- if mod(i,model.step)==0
- fprintf('#%.0f SV:%5.2f(%d)\tQueried Labels:%5.2f(%d)\tAER:%5.2f\n', ...
- ceil(i/1000),numel(model.S)/model.iter*100,numel(model.S),...
- model.numQueries/model.iter*100,model.numQueries,...
- model.aer(model.iter)*100);
- end
- end
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