• Denna
    了解作者
  • matlab
    开发工具
  • 545KB
    文件大小
  • rar
    文件格式
  • 0
    收藏次数
  • 1 积分
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  • 1
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  • 2017-08-13 15:51
    上传日期
模式识别matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值
rzcrgnition.rar
  • Other
  • Edit_Distance.m
    557B
  • HMM_Boltzmann.m
    4.1KB
  • contents.m
    1.5KB
  • Stochastic_Regression.m
    1.1KB
  • MultipleDiscriminantAnalysis.m
    1.1KB
  • sufficient_statistics.m
    1023B
  • Boyer_Moore_String_Matching.m
    989B
  • Bayes_belief_net.mat
    1.5KB
  • demo_fun.m
    47B
  • Naive_String_Matching.m
    451B
  • Grammatical_Inference.m
    4.2KB
  • sample_hmm.mat
    528B
  • gradient_descent.m
    800B
  • HMM_Forward_Backward.m
    1.8KB
  • ROCC.m
    828B
  • Newton_descent.m
    1.3KB
  • HMM_Backward.m
    1008B
  • HMM_Decoding.m
    881B
  • Bayesian_parameter_est.m
    1.6KB
  • mean_jackknife.m
    527B
  • Bayesian_Belief_Networks.m
    4.6KB
  • Bottom_Up_Parsing.m
    2KB
  • HMM_generate.m
    907B
  • HMM_Forward.m
    1022B
  • high_histogram.m
    2.2KB
  • mean_bootstrap.m
    747B
  • HMM_Evaluation.m
    1.1KB
  • Genetic_Programming.m
    8.4KB
  • User guide.pdf
    293.8KB
  • generate_data_set.m
    2.6KB
  • Genetic_Algorithm.m
    2.9KB
  • Deterministic_annealing.m
    3KB
  • Genetic_Culling.m
    3.7KB
  • Nearest_Neighbor.m
    1.1KB
  • Backpropagation_SM.m
    3.1KB
  • classifier.mat
    4.2KB
  • Infomat.m
    1.9KB
  • predict_performance.m
    1.7KB
  • Backpropagation_Quickprop.m
    6.3KB
  • RBF_Network.m
    1.4KB
  • loglikelihood.m
    435B
  • Relaxation_BM.m
    1.5KB
  • None.m
    342B
  • Pocket.m
    2.6KB
  • ML_II.m
    2.8KB
  • FindParametersFunctions.m
    6.2KB
  • Relaxation_SSM.m
    1.5KB
  • contents.m
    9.3KB
  • Interactive_Learning.m
    2.7KB
  • Cascade_Correlation.m
    5.3KB
  • Backpropagation_Stochastic.m
    2.9KB
  • fuzzy_k_means.m
    1.8KB
  • tAda_Boost.m
    2.1KB
  • LVQ3.m
    2.6KB
  • Perceptron_BVI.m
    1.3KB
  • Preprocessing.txt
    793B
  • Perceptron_Batch.m
    1.3KB
  • Ho_Kashyap.m
    1.8KB
  • feature_selection_commands.m
    1.8KB
  • FindParameters.m
    18.9KB
  • CART.m
    4KB
  • ICA.m
    1.8KB
  • ID3.m
    4.3KB
  • Competitive_learning.m
    2.8KB
  • Stochastic_SA.m
    3.4KB
  • Voted_Perceptron.m
    4.2KB
  • PNN.m
    1.6KB
  • PCA.m
    969B
  • FishersLinearDiscriminant.m
    836B
  • Koller.m
    1.4KB
  • min_spanning_tree.m
    3.8KB
  • calculate_error.m
    758B
  • HDR.m
    1.3KB
  • LVQ1.m
    2.2KB
  • UAbout.bmp
    930.5KB
  • ML.m
    895B
  • Deterministic_SA.m
    2.6KB
  • GaussianParameters.m
    7.4KB
  • C4_5.m
    5.8KB
  • Marginalization.m
    1.7KB
  • voronoi_regions.m
    723B
  • Discriminability.m
    492B
  • plot_process.m
    319B
  • Chernoff.m
    902B
  • Bayesian_Model_Comparison.m
    3.2KB
  • Optimal_Brain_Surgeon.m
    3.4KB
  • AGHC.m
    4.2KB
  • Kohonen_SOFM.m
    2.5KB
  • enter_distributions.mat
    1.9KB
  • k_means.m
    1.8KB
  • seperable.mat
    16.9KB
  • Gibbs.m
    3KB
  • SVM.m
    5.4KB
  • FindParameters.mat
    2.9KB
  • Perceptron_FM.m
    1.5KB
  • Minimum_Cost.m
    1.1KB
  • multialgorithms_commands.m
    9.5KB
  • process_params.m
    2.8KB
  • LocBoost.m
    9.2KB
内容介绍
<html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta charset="utf-8"> <meta name="generator" content="pdf2htmlEX"> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> <link rel="stylesheet" href="https://static.pudn.com/base/css/base.min.css"> <link rel="stylesheet" href="https://static.pudn.com/base/css/fancy.min.css"> <link rel="stylesheet" href="https://static.pudn.com/prod/directory_preview_static/625411a674bc5c01051307a0/raw.css"> <script src="https://static.pudn.com/base/js/compatibility.min.js"></script> <script src="https://static.pudn.com/base/js/pdf2htmlEX.min.js"></script> <script> try{ pdf2htmlEX.defaultViewer = new pdf2htmlEX.Viewer({}); }catch(e){} </script> <title></title> </head> <body> <div id="sidebar" style="display: none"> <div id="outline"> </div> </div> <div id="pf1" class="pf w0 h0" data-page-no="1"><div class="pc pc1 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="https://static.pudn.com/prod/directory_preview_static/625411a674bc5c01051307a0/bg1.jpg"><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">1</div><div class="t m0 x2 h3 y2 ff1 fs1 fc0 sc0 ls1 ws0">Classification<span class="_ _0"> </span>Toolbox</div><div class="t m0 x3 h4 y3 ff1 fs2 fc0 sc0 ls2 ws0">For<span class="_ _1"> </span>use<span class="_ _1"> </span>with<span class="_ _1"> </span>MATLAB</div><div class="t m0 x4 h5 y4 ff1 fs3 fc0 sc0 ls0 ws0">&#174;</div><div class="t m0 x5 h6 y5 ff1 fs4 fc0 sc0 ls3 ws0">D<span class="_ _2"></span>a<span class="_ _2"></span>v<span class="_ _2"></span>i<span class="_ _2"></span>dG<span class="_ _2"></span>.S<span class="_ _2"></span>t<span class="_ _3"></span>o<span class="_ _3"></span>r<span class="_ _2"></span>k</div><div class="t m0 x5 h6 y6 ff1 fs4 fc0 sc0 ls4 ws0">Elad<span class="_ _4"> </span>Yom-Tov</div><div class="t m0 x6 h7 y7 ff1 fs5 fc0 sc0 ls5 ws0">User&#8217;s<span class="_ _5"> </span>Guide</div></div><div class="pi" data-data='{"ctm":[1.611639,0.000000,0.000000,1.611639,0.000000,0.000000]}'></div></div> </body> </html>
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