Description: k nearest neighbours, this is a method to design which cluster the test sample belong to using the KNN algorithm,which is a matlab code worth using and download.
- [MATLABArsenal] - a Matlab tool kits, including some insid
- [Classification-MatLab-Toolbox] - pattern recognition Matlab toolbox, incl
- [ModuleReconizeKNNalgorithm] - KNN is an effective pattern recognition
- [hyperspectral] - Field: Digital Image Processing i)Readin
- [knn] - knn classifier in C,5 simple examples ar
- [SVM] - In this paper, we show how support vecto
- [61] - The Research of Vehicle Classification U
- [PR] - Pattern recognition toolbox functions, i
- [spider] - the latest machine learning matlab toolb
- [nearestneighbour] - Compute nearest neighbours (by Euclidean
File list (Check if you may need any files):
SVM-KM\AdaptScalSVM\costlbfixed.m
......\............\costlfixed.m
......\............\costwbfixed.m
......\............\costwfixed.m
......\............\ExampledemoAdaptScal.m
......\............\ExampleFeatSelAdaptScal.m
......\............\gradlbfixed.m
......\............\gradlfixed.m
......\............\gradwbfixed.m
......\............\gradwfixed.m
......\............\LagrangeUpdate.m
......\............\SigmaUpdate.m
......\............\svmfit.asv
......\............\svmfit.m
......\............\svmfitconj.m
......\contents.m
......\cout.m
......\dataset1.mat
......\dataset2.mat
......\dataset3.mat
......\datasets.m
......\featselreg\exfeatselreg1.m
......\..........\FeatSelregalpha.m
......\..........\FeatSelregalphaGD.m
......\..........\FeatSelregalphaGDrandom.m
......\..........\FeatSelreglinearL1.m
......\..........\FeatSelregmargin.m
......\..........\FeatSelregmarginGD.m
......\..........\FeatSelregmarginGDrandom.m
......\..........\FeatSelregr2w2.m
......\..........\FeatSelregr2w2GD.m
......\..........\FeatSelregr2w2GDrandom.m
......\..........\FeatSelregspanbound.m
......\..........\FeatSelregspanboundGD.m
......\..........\FeatSelregspanboundGDrandom.m
......\..........\r2alpharegL2.m
......\..........\spanestimateregL2.m
......\FeatureSelection\featselcorrcoeff.m
......\................\featselkernelderivative.m
......\................\FeatSelmargdif.m
......\................\FeatSelmargdif1v1.m
......\................\FeatSelmargin.m
......\................\FeatSelr2w2.m
......\................\FeatSelr2w2diff.m
......\fileaccess.m
......\functioneval.m
......\gda.m
......\givrot.m
......\kbp\BuildTrapScale.m
......\...\calcdistance.m
......\...\CalcTrapScale.m
......\...\exlar.m
......\...\exlar1.m
......\...\exlarrealdata.m
......\...\exlarsignalclassif.m
......\...\exmultikernellarclass.m
......\...\HingeLAR.m
......\...\HingeLAR2.m
......\...\LAR.m
......\...\LARval.m
......\...\multiplekernel.m
......\...\normalizekernelLAR.m
......\...\plot2Ddec.m
......\...\pyrim.mat
......\...\testHingeLAR.m
......\kernelpca.m
......\kernelpcaproj.m
......\kernelset.m
......\libsvminterface\mexSVMClass.dll
......\...............\mexSVMClass.mexglx
......\...............\mexSVMTrain.dll
......\...............\mexSVMTrain.mexglx
......\...............\svmclasslib.m
......\...............\svmvallib.m
......\license.txt
......\LPsvmclass.m
......\LPsvmreg.m
......\monqp.m
......\monqpCinfty.m
......\normalizekernel.m
......\phispan.m
......\r2smallestsphere.m
......\.egpath\exregpathoneclasssvm.asv
......\.......\exregpathoneclasssvm.m
......\.......\regpathsvmoneclass.m
......\.......\TransformPathFromNu.m
......\regsolve.m
......\rncalc.m
......\rnval.m
......\spanestimate.m
......\svmclass.asv
......\svmclass.m
......\svmclassL2.m
......\svmclassL2LS.m
......\svmclassLS.m
......\svmclassnpa.m
......\svmkernel.m
......\svmmulticlass.m
......\svmmulticlassoneagainstall.m
......\svmmulticlassoneagainstone.m
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