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    发布日期: 2021-05-24
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超全的模式识别Matlab源程序,包括很多方面,希望对大家有用

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function [test_targets E] = ada_boost(train_patterns train_targets test_patterns params)

% Classify using the AdaBoost algorithm
% Inputs:
%  train_patterns - Train patterns
% train_targets - Train targets
%   test_patterns   - Test  patterns
% Params - [NumberOfIterations Weak Learner Type Learner‘s parameters]
%
% Outputs
% test_targets - Predicted targets
%   E               - Errors through the iterations
%
% NOTE: Suitable for only two classes
%

[k_max weak_learner alg_param] = process_params(params);

[NiM] = size(train_patterns);
W   = ones(1M)/M;
IterDisp = 10;

full_patterns   = [train_patterns test_patterns];
test_targets    = zeros(1 size(test_patterns2));

%Do the AdaBoosting
for k = 1:k_max
   %Train weak learner Ck using the data sampled according to W:
   %...so sample the data according to W
   randnum = rand(1M);
   cW    = cumsum(W);
   indices = zeros(1M);
   for i = 1:M
      %Find which bin the random number falls into
      loc = max(find(randnum(i) > cW))+1;
      if isempty(loc)
         indices(i) = 1;
      else
         indices(i) = loc;
      end
   end
   
   %...and now train the classifier
   Ck  = feval(weak_learner train_patterns(: indices) train_targets(indices) full_patterns alg_param);

   %Ek <- Training error of Ck 
   E(k) = sum(W.*(Ck(1:M) ~= train_targets));
   
   if (E(k) == 0)
      break
   end
   
   %alpha_k <- 1/2*ln(1-Ek)/Ek)
   alpha_k = 0.5*log((1-E(k))/E(k));
   
   %W_k+1 = W_k/Z*exp(+/-alpha)
   W  = W.*exp(alpha_k*(xor(Ck(1:M)train_targets)*2-1));
   W  = W./sum(W);
   
   %Update the test targets
   test_targets  = test_targets + alpha_k*(2*Ck(M+1:end)-1);
   
   if (k/IterDisp == floor(k/IterDisp))
      disp([‘Completed ‘ num2str(k) ‘ boosting iterations‘])
   end
   
end

test_targets = test_targets > 0;

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----

     文件     952782  2002-05-20 08:36  PR\About.bmp

     文件       1892  2003-06-26 20:48  PR\Ada_Boost.m

     文件       2815  2003-06-10 20:38  PR\ADDC.m

     文件       4439  2003-08-31 20:47  PR\AGHC.m

     文件       3397  2003-06-26 21:06  PR\Backpropagation_Batch.m

     文件       5308  2003-06-26 21:06  PR\Backpropagation_CGD.m

     文件       6494  2003-02-22 20:26  PR\Backpropagation_Quickprop.m

     文件       5029  2003-02-22 20:42  PR\Backpropagation_Recurrent.m

     文件       3334  2003-02-22 20:46  PR\Backpropagation_SM.m

     文件       3117  2003-02-22 20:50  PR\Backpropagation_Stochastic.m

     文件       1421  2003-03-05 19:07  PR\Balanced_Winnow.m

     文件       3343  2003-04-02 20:27  PR\Bayesian_Model_Comparison.m

     文件        588  2001-12-27 09:56  PR\Bhattacharyya.m

     文件       3248  2003-03-09 22:07  PR\BIMSEC.m

     文件       5599  2003-06-10 20:37  PR\C4_5.m

     文件        905  2003-02-22 21:13  PR\calculate_error.m

     文件        756  2003-02-18 17:57  PR\calculate_region.m

     文件       3721  2003-11-06 22:06  PR\CART.m

     文件        846  2003-02-22 21:36  PR\CARTfunctions.m

     文件       5527  2003-02-22 21:45  PR\Cascade_Correlation.m

     文件        902  2001-12-27 10:15  PR\Chernoff.m

     文件       2745  2003-11-08 22:59  PR\Classification.txt

     文件       1995  2003-02-22 21:48  PR\classification_error.m

     文件      19454  2003-09-21 21:04  PR\classifier.m

     文件       4520  2003-03-04 20:47  PR\classifier.mat

     文件      26413  2004-07-18 20:28  PR\classifier_commands.m

     文件       2448  2004-07-18 20:21  PR\classify_paramteric.m

     文件       3507  2003-10-11 22:11  PR\click_points.m

     文件        753  2003-08-07 11:49  PR\combinations.m

     文件       2754  2003-03-09 22:09  PR\Competitive_learning.m

............此处省略151个文件信息

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