超全的模式识别matlab源码程序 - matlab模式识别 - 谷速源码

超全的模式识别matlab源码程序 - matlab模式识别 - 谷速源码

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标题：超全的模式识别matlab源码程序
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所属分类: 模式识别资源类型：程序源码文件大小: 376.69 KB 上传时间: 2019-06-16 21:46:29 下载次数: 543 资源积分：1分提供者: zhangsan456 code

内容：
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;

文件列表（点击上边下载按钮，如果是垃圾文件请在下面评价差评或者投诉）:

code/
code/Other/
code/datasets/
code/ADDC.m
code/AGHC.m
code/About.bmp
code/Ada_Boost.m
code/BIMSEC.m
code/Backpropagation_Batch.m
code/Backpropagation_CGD.m
code/Backpropagation_Quickprop.m
code/Backpropagation_Recurrent.m
code/Backpropagation_SM.m
code/Backpropagation_Stochastic.m
code/Balanced_Winnow.m
code/Bayesian_Model_Comparison.m
code/Bhattacharyya.m
code/C4_5.m
code/CART.m
code/CARTfunctions.m
code/Cascade_Correlation.m
code/Chernoff.m
code/Classification.txt
code/Competitive_learning.m
code/Components_with_DF.m
code/Components_without_DF.m
code/DSLVQ.m
code/Deterministic_Boltzmann.m
code/Deterministic_SA.m
code/Deterministic_annealing.m
code/Discrete_Bayes.m
code/Discriminability.m
code/EM.m
code/Exhaustive_Feature_Selection.m
code/Feature_selection.txt
code/FindParameters.m
code/FindParameters.mat
code/FindParametersFunctions.m
code/FishersLinearDiscriminant.m
code/GaussianParameters.m
code/GaussianParameters.mat
code/Genetic_Algorithm.m
code/Genetic_Culling.m
code/Genetic_Programming.m
code/Gibbs.m
code/HDR.m
code/Ho_Kashyap.m
code/ICA.m
code/ID3.m
code/Infomat.m
code/Information_based_selection.m
code/Interactive_Learning.m
code/Kohonen_SOFM.m
code/LMS.m
code/LS.m
code/LVQ1.m
code/LVQ3.m
code/Leader_Follower.m
code/LocBoost.m
code/LocBoostFunctions.m
code/Local_Polynomial.m
code/MDS.m
code/ML.m
code/ML_II.m
code/ML_diag.m
code/Marginalization.m
code/Minimum_Cost.m
code/MultipleDiscriminantAnalysis.m
code/Multivariate_Splines.m
code/NDDF.m
code/NLCA.m
code/NearestNeighborEditing.m
code/Nearest_Neighbor.m
code/None.m
code/Optimal_Brain_Surgeon.m
code/Other/Bayes_belief_net.mat
code/Other/Bayesian_Belief_Networks.m
code/Other/Bayesian_parameter_est.m
code/Other/Bottom_Up_Parsing.m
code/Other/Boyer_Moore_String_Matching.m
code/Other/Edit_Distance.m
code/Other/Grammatical_Inference.m
code/Other/HMM_Backward.m
code/Other/HMM_Boltzmann.m
code/Other/HMM_Decoding.m
code/Other/HMM_Evaluation.m
code/Other/HMM_Forward.m
code/Other/HMM_Forward_Backward.m
code/Other/HMM_generate.m
code/Other/Naive_String_Matching.m
code/Other/Newton_descent.m
code/Other/ROCC.m
code/Other/Stochastic_Regression.m
code/Other/contents.m
code/Other/demo_fun.m
code/Other/gradient_descent.m
code/Other/high_histogram.m
code/Other/mean_bootstrap.m
code/Other/mean_jackknife.m
code/Other/sample_hmm.mat
code/Other/sufficient_statistics.m
code/PCA.m
code/PNN.m
code/Parzen.m
code/Perceptron.m
code/Perceptron_BVI.m
code/Perceptron_Batch.m
code/Perceptron_FM.m
code/Perceptron_VIM.m
code/Perceptron_Voted.m
code/Pocket.m
code/Preprocessing.txt
code/Projection_Pursuit.m
code/RBF_Network.m
code/RCE.m
code/RDA.m
code/README.txt
code/Relaxation_BM.m
code/Relaxation_SSM.m
code/SOHC.m
code/SVM.m
code/Scaling_transform.m
code/Sequential_Feature_Selection.m
code/Stochastic_SA.m
code/Store_Grabbag.m
code/Stumps.m
code/Whitening_transform.m
code/calculate_error.m
code/calculate_region.m
code/classification_error.m
code/classifier.m
code/classifier.mat
code/classifier_commands.m
code/classify_paramteric.m
code/click_points.m
code/combinations.m
code/contents.m
code/datasets/DHS_cover.mat
code/datasets/DHSchapter10.mat
code/datasets/DHSchapter2.mat
code/datasets/DHSchapter3.mat
code/datasets/DHSchapter4.mat
code/datasets/DHSchapter5.mat
code/datasets/DHSchapter6.mat
code/datasets/DHSchapter7.mat
code/datasets/DHSchapter8.mat
code/datasets/DHSchapter9.mat
code/datasets/XOR.mat
code/datasets/chess.mat
code/datasets/clouds.mat
code/datasets/four_spiral.mat
code/datasets/spiral.mat
code/enter_distributions.m
code/enter_distributions.mat
code/enter_distributions_commands.m
code/feature_selection.m
code/feature_selection.mat
code/feature_selection_commands.m
code/fuzzy_k_means.m
code/generate_data_set.m
code/high_histogram.m
code/k_means.m
code/load_file.m
code/loglikelihood.m
code/make_a_draw.m
code/min_spanning_tree.m
code/multialgorithms.m
code/multialgorithms.mat
code/multialgorithms_commands.m
code/plot_process.m
code/plot_scatter.m
code/predict_performance.m
code/process_params.m
code/read_algorithms.m
code/show_algorithms.m
code/start_classify.m
code/synthetic.mat
code/voronoi_regions.m

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