KN近邻算法

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  • 2022-04-24 09:45
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KNN近邻算法.zip
  • KNN近邻算法
  • 模式识别作业——用平均样本法,平均距离法,最近邻法和K近邻法进行分类
  • Classify_Homework.m
    7.5KB
  • K_average
  • KindDisplay.asv
    336B
  • Clustering.m
    562B
  • Clustering.asv
    517B
  • main.asv
    112B
  • KindDisplay.m
    257B
  • Display.m
    99B
  • main.m
    820B
  • CaculateCenter.asv
    262B
  • CaculateCenter.m
    245B
  • k-nearest neighbour—MATLAB
  • excel example
  • K-NearestNeighbors.xls
    24KB
  • KNN_TimeSeries.xls
    16.5KB
  • KNN_TimeSeries for extrapolation .xls
    16.5KB
  • KNN_Smoothing.xls
    60.5KB
  • K-NearestNeighbors for classification.xls
    24KB
  • KNN_Smoothing for inerpolation.xls
    53.5KB
  • kNearestNeighbors.m
    1.4KB
  • Classification MatLab Toolbox
  • Other
  • Boyer_Moore_String_Matching.m
    989B
  • Grammatical_Inference.m
    4.2KB
  • gradient_descent.m
    800B
  • ROCC.m
    828B
  • HMM_Backward.m
    1008B
  • HMM_Forward.m
    1022B
  • contents.m
    1.5KB
  • HMM_Forward_Backward.m
    1.8KB
  • demo_fun.m
    47B
  • Bottom_Up_Parsing.m
    2KB
  • Edit_Distance.m
    557B
  • Newton_descent.m
    1.3KB
  • Bayes_belief_net.mat
    1.5KB
  • HMM_Decoding.m
    881B
  • mean_bootstrap.m
    747B
  • HMM_generate.m
    907B
  • Bayesian_parameter_est.m
    1.6KB
  • HMM_Boltzmann.m
    4.1KB
  • Stochastic_Regression.m
    1.1KB
  • Naive_String_Matching.m
    451B
  • sufficient_statistics.m
    1023B
  • Bayesian_Belief_Networks.m
    4.6KB
  • MultipleDiscriminantAnalysis.m
    1.1KB
  • mean_jackknife.m
    527B
  • sample_hmm.mat
    528B
  • HMM_Evaluation.m
    1.1KB
  • high_histogram.m
    2.2KB
  • enter_distributions_commands.m
    5.8KB
  • Parzen.m
    1.3KB
  • ML_II.m
    2.8KB
  • enter_distributions.mat
    1.9KB
  • Marginalization.m
    1.7KB
  • Infomat.m
    1.9KB
  • Stumps.m
    2.1KB
  • plot_process.m
    319B
  • Bayesian_Model_Comparison.m
    3.2KB
  • Relaxation_BM.m
    1.5KB
  • feature_selection.mat
    1.7KB
  • classifier_commands.m
    25.1KB
  • C4_5.m
    5.8KB
  • Preprocessing.txt
    793B
  • CART.m
    4KB
  • MDS.m
    2.5KB
  • voronoi_regions.m
    723B
  • Discrete_Bayes.m
    2KB
  • multialgorithms_commands.m
    9.5KB
  • ICA.m
    1.8KB
  • seperable.mat
    16.9KB
  • Genetic_Algorithm.m
    2.9KB
  • SVM.m
    5.4KB
  • start_classify.m
    7.3KB
  • Koller.m
    1.4KB
  • Gibbs.m
    3KB
  • RCE.m
    1.6KB
  • Discriminability.m
    492B
  • Components_with_DF.m
    2.3KB
  • k_means.m
    1.8KB
  • Backpropagation_CGD.m
    5.1KB
  • find_classes.m
    172B
  • LVQ1.m
    2.2KB
  • GaussianParameters.m
    7.4KB
  • clouds.mat
    84.1KB
  • ID3.m
    4.3KB
  • chess.mat
    1.5KB
  • contents.m
    9.3KB
  • ML.m
    895B
  • Cascade_Correlation.m
    5.3KB
  • Leader_Follower.m
    2.6KB
  • multialgorithms.m
    6.4KB
  • Perceptron_Batch.m
    1.3KB
  • LVQ3.m
    2.6KB
  • Perceptron_BVI.m
    1.3KB
  • Local_Polynomial.m
    2KB
  • FindParameters.mat
    2.9KB
  • EM.m
    4.9KB
  • Nearest_Neighbor.m
    1.1KB
内容介绍
======================================================================== CONSOLE APPLICATION : KNN ======================================================================== AppWizard has created this KNN application for you. This file contains a summary of what you will find in each of the files that make up your KNN application. KNN.dsp This file (the project file) contains information at the project level and is used to build a single project or subproject. Other users can share the project (.dsp) file, but they should export the makefiles locally. KNN.cpp This is the main application source file. ///////////////////////////////////////////////////////////////////////////// Other standard files: StdAfx.h, StdAfx.cpp These files are used to build a precompiled header (PCH) file named KNN.pch and a precompiled types file named StdAfx.obj. ///////////////////////////////////////////////////////////////////////////// Other notes: AppWizard uses "TODO:" to indicate parts of the source code you should add to or customize. /////////////////////////////////////////////////////////////////////////////
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