Classification-MatLab-Toolbox

代码说明：

  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(pattern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value)

文件列表：

Classification MatLab Toolbox

.............................\Ada_Boost.m
.............................\ADDC.m
.............................\AGHC.m
.............................\Backpropagation_Batch.m
.............................\Backpropagation_CGD.m
.............................\Backpropagation_Quickprop.m
.............................\Backpropagation_Recurrent.m
.............................\Backpropagation_SM.m
.............................\Backpropagation_Stochastic.m
.............................\Balanced_Winnow.m
.............................\Bayesian_Model_Comparison.m
.............................\Bhattacharyya.m
.............................\BIMSEC.m
.............................\C4_5.m
.............................\calculate_error.m
.............................\calculate_region.m
.............................\CART.m
.............................\CARTfunctions.m
.............................\Cascade_Correlation.m
.............................\Chernoff.m
.............................\chess.mat
.............................\Classification.txt
.............................\classification_error.m
.............................\classifier.m
.............................\classifier.mat
.............................\classifier_commands.m
.............................\click_points.m
.............................\clouds.mat
.............................\Competitive_learning.m
.............................\Components_without_DF.m
.............................\Components_with_DF.m
.............................\contents.m
.............................\decision_region.m
.............................\Deterministic_annealing.m
.............................\Deterministic_Boltzmann.m
.............................\Deterministic_SA.m
.............................\Discrete_Bayes.m
.............................\Discriminability.m
.............................\DSLVQ.m
.............................\EM.m
.............................\enter_distributions.m
.............................\enter_distributions.mat
.............................\enter_distributions_commands.m
.............................\feature_selection.m
.............................\feature_selection.mat
.............................\Feature_selection.txt
.............................\feature_selection_commands.m
.............................\FindParameters.m
.............................\FindParameters.mat
.............................\FindParametersFunctions.m
.............................\find_classes.m
.............................\FishersLinearDiscriminant.m
.............................\fuzzy_k_means.m
.............................\GaussianParameters.m
.............................\GaussianParameters.mat
.............................\generate_data_set.m
.............................\Genetic_Algorithm.m
.............................\Genetic_Culling.m
.............................\Genetic_Programming.m
.............................\Gibbs.m
.............................\HDR.m
.............................\high_histogram.m
.............................\Ho_Kashyap.m
.............................\ICA.m
.............................\ID3.m
.............................\Infomat.m
.............................\Interactive_Learning.m
.............................\Kohonen_SOFM.m
.............................\Koller.m
.............................\k_means.m
.............................\Leader_Follower.m
.............................\LMS.m
.............................\load_file.m
.............................\Local_Polynomial.m
.............................\LocBoost.m
.............................\LocBoostFunctions.m
.............................\loglikelihood.m
.............................\LS.m
.............................\LVQ1.m
.............................\LVQ3.m
.............................\make_a_draw.m
.............................\Marginalization.m
.............................\MDS.m
.............................\Minimum_Cost.m
.............................\min_spanning_tree.m
.............................\ML.m
.............................\ML_diag.m
.............................\ML_II.m
.............................\multialgorithms.m
.............................\multialgorithms.mat
.............................\multialgorithms_commands.m
.............................\Multivariate_Splines.m
.............................\NDDF.m
.............................\NearestNeighborEditing.m
.............................\Nearest_Neighbor.m
.............................\NLPCA.m
.............................\None.m
.............................\Optimal_Brain_Surgeon.m