文件名称:statistic_pattern_reconnition_toolbox

介绍说明--下载内容均来自于网络,请自行研究使用

模式识别中统计模式识别的方法,包括贝叶斯,统计学习,LDA,PCA,SVM的经典方法,是不可多得的算法的toolbox-pattern recognition statistical pattern recognition methods, including Bayesian statistics study, LDA, PCA, SVM classical method, the algorithm is rare in toolbox
(系统自动生成,下载前可以参看下载内容)

下载文件列表

stprtool

........\bayes

........\.....\bayescln.m

........\.....\bayescln.m~

........\.....\bayesdemo1.m

........\.....\bayesdemo2.m

........\.....\bayesdemo3.m

........\.....\bayeserr.m

........\.....\bayesnd.m

........\.....\bhattach.m

........\.....\Contents.m

........\.....\gaussc2.mat

........\.....\gaussc2b.mat

........\.....\pbayescln.m

........\Contents.m

........\Contents.m~

........\datasets

........\........\ascii2xi.m

........\........\ascii2xi.m~

........\........\checkdat.m

........\........\chgnum.m

........\........\Contents.m

........\........\Contents.m~

........\........\creatset.m

........\........\datafiles.m

........\........\dataid.m

........\........\img2x.m

........\........\img2xi.m

........\........\nmix.m

........\........\norm2file.m

........\........\pgm2x.m

........\........\pgm2x.m~

........\........\pgm2xi.m

........\........\pimg.m

........\........\pimg.m~

........\........\sortxi.m

........\........\x2img.m

........\........\x2pgm.m

........\........\xi2file.m

........\generalp

........\........\acov.m

........\........\cerror.m

........\........\cliplin1.m

........\........\cliplin2.m

........\........\clrchild.m

........\........\cmp1.mat

........\........\cmpwin.m

........\........\color.m

........\........\Contents.m

........\........\crossval.m

........\........\ellips.m

........\........\erfc2.m

........\........\fig2eps.m

........\........\getaxis.m

........\........\itosgn.m

........\........\kernel1.m

........\........\knnclass.c

........\........\knnclass.m

........\........\knnclass.mexlx

........\........\knnclass.m~

........\........\mahalan.m

........\........\marker.m

........\........\multi2dicho.m

........\........\normald.m

........\........\pdiscrim.m

........\........\pdiscrim.m~

........\........\pline.m

........\........\pnmix.m

........\........\pnormal.m

........\........\ppatterns.m

........\........\ppoints.m

........\........\randpds.m

........\........\setaxis.m

........\........\sgntoi.m

........\learning

........\........\Contents.m

........\........\minimax

........\........\.......\Contents.m

........\........\.......\mmdata1.mat

........\........\.......\mmdata2.mat

........\........\.......\mmdemo.m

........\........\.......\mmln.m

........\........\unsuper

........\........\.......\clusters2.mat

........\........\.......\clusters2over.mat

........\........\.......\clusters4.mat

........\........\.......\Contents.m

........\........\.......\mix4init1.mat

........\........\.......\mix4init2.mat

........\........\.......\mln.m

........\........\.......\unsudemo.m

........\........\.......\unsund.m

........\........\.......\unsuni.m

........\linear

........\......\anderson

........\......\........\andrdata1.mat

........\......\........\andrdata2.mat

........\......\........\andrdemo.m

........\......\........\andrerr.m

........\......\........\Contents.m

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