文件名称:Classification-MatLab-Toolbox

  • 所属分类:
  • matlab例程
  • 资源属性:
  • [Matlab] [源码]
  • 上传时间:
  • 2012-11-26
  • 文件大小:
  • 616kb
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  • 0次
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  • l***
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数据挖掘中用于分类的算法的MATLAB源程序代码。-Data mining algorithms for classification MATLAB source code.
(系统自动生成,下载前可以参看下载内容)

下载文件列表

Classification MatLab Toolbox\spiral.mat

.............................\ADDC.m

.............................\AGHC.m

.............................\load_file.m

.............................\About.bmp

.............................\make_a_draw.m

.............................\Ada_Boost.m

.............................\BIMSEC.m

.............................\feature_selection.m

.............................\Backpropagation_Batch.m

.............................\feature_selection.mat

.............................\Backpropagation_CGD.m

.............................\enter_distributions_commands.m

.............................\Backpropagation_Quickprop.m

.............................\feature_selection_commands.m

.............................\Backpropagation_Recurrent.m

.............................\find_classes.m

.............................\Backpropagation_SM.m

.............................\fuzzy_k_means.m

.............................\Backpropagation_Stochastic.m

.............................\generate_data_set.m

.............................\Balanced_Winnow.m

.............................\high_histogram.m

.............................\Bayesian_Model_Comparison.m

.............................\loglikelihood.m

.............................\Bhattacharyya.m

.............................\C4_5.m

.............................\CART.m

.............................\min_spanning_tree.m

.............................\CARTfunctions.m

.............................\multialgorithms.m

.............................\Cascade_Correlation.m

.............................\seperable.mat

.............................\Chernoff.m

.............................\multialgorithms.mat

.............................\Classification.txt

.............................\multialgorithms_commands.m

.............................\Competitive_learning.m

.............................\plot_process.m

.............................\Components_with_DF.m

.............................\plot_scatter.m

.............................\Components_without_DF.m

.............................\DSLVQ.m

.............................\predict_performance.m

.............................\Deterministic_Boltzmann.m

.............................\process_params.m

.............................\Deterministic_SA.m

.............................\read_algorithms.m

.............................\Deterministic_annealing.m

.............................\start_classify.m

.............................\Discrete_Bayes.m

.............................\voronoi_regions.m

.............................\Discriminability.m

.............................\EM.m

.............................\Other\Bayes_belief_net.mat

.............................\.....\Bayesian_Belief_Networks.m

.............................\.....\Bayesian_parameter_est.m

.............................\.....\Bottom_Up_Parsing.m

.............................\.....\Boyer_Moore_String_Matching.m

.............................\.....\Edit_Distance.m

.............................\.....\Grammatical_Inference.m

.............................\.....\HMM_Backward.m

.............................\.....\HMM_Boltzmann.m

.............................\.....\HMM_Decoding.m

.............................\.....\HMM_Evaluation.m

.............................\.....\HMM_Forward.m

.............................\.....\HMM_Forward_Backward.m

.............................\.....\HMM_generate.m

.............................\.....\MultipleDiscriminantAnalysis.m

.............................\.....\Naive_String_Matching.m

.............................\.....\Newton_descent.m

.............................\.....\ROCC.m

.............................\.....\Stochastic_Regression.m

.............................\.....\contents.m

.............................\.....\demo_fun.m

.............................\.....\gradient_descent.m

.............................\.....\high_histogram.m

.............................\.....\mean_bootstrap.m

.............................\.....\mean_jackknife.m

.............................\.....\sample_hmm.mat

.............................\.....\sufficient_statistics.m

...................

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