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function [test_targets, a] = Relaxation_BM(train_patterns, train_targets, test_patterns, params) % Classify using the batch relaxation with margin algorithm % Inputs: % train_patterns - Train patterns % train_targets - Train targets % test_patterns - Test patterns % param - [Max iter, Margin, Convergence rate] % % Outputs % test_targets - Predicted targets % a - Classifier weights % % NOTE: Works for only two classes. [c, n] = size(train_patterns); [Max_iter, b, eta] = process_params(params); y = [train_patterns ; ones(1,n)]; train_zero = find(train_targets == 0); %Preprocessing processed_patterns = y; processed_patterns(:,train_zero) = -processed_patterns(:,train_zero); %Initial weights a = sum(processed_patterns')'; iter = 0; Yk = [1]; while (~isempty(Yk) & (iter < Max_iter)) iter = iter + 1; %If a'y_j <= b then append y_j to Yk Yk = []; for k = 1:n, if (a'*processed_patterns(:,k) <= b), Yk = [Yk k]; end end if isempty(Yk), break end % a <- a + eta*sum((b-w'*Yk)/||Yk||*Yk) grad = (b-a'*y(:,Yk))./sum(y(:,Yk).^2); update = sum(((ones(c+1,1)*grad).*y(:,Yk))')'; a = a + eta * update; end if (iter == Max_iter), disp(['Maximum iteration (' num2str(Max_iter) ') reached']); end %Classify test patterns test_targets = a'*[test_patterns; ones(1, size(test_patterns,2))] > 0;