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文件名称:svm

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    2013-10-05
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    3.41mb
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支持向量机方法是建立在统计学习理论的VC维理论和结构风险最小原理基础上的,很不错的程序-Support vector machine (SVM) method is based on statistical learning theory of VC dimension theory and structure risk minimum principle, on the basis of good programs


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下载文件列表

svm/binomial.m
svm/centrefig.m
svm/cj/bx19-18.txt
svm/cj/bx19-18wx.txt
svm/cj/bx22-15wx.txt
svm/cj/bx31-16.txt
svm/cj/bx31-16wx.txt
svm/cj/bx38-14.txt
svm/cj/bx38-14wx.txt
svm/cj/bx40-14.txt
svm/cj/bx40-14wx.txt
svm/cj/bx43-6.txt
svm/cj/bx43-6wx.txt
svm/cj/sh4.mat
svm/cj/sh5.mat
svm/cj/shuju1.mat
svm/cj/shuju2.mat
svm/cj/si31(38.mat
svm/cj/snorm3.mat
svm/cj/sy3.mat
svm/cj/SY4.mat
svm/cj/x13-16.txt
svm/cj/x13-16wx.txt
svm/cj/x19-18/bx19-18.txt
svm/cj/x19-18/bx19-18wx.txt
svm/cj/x19-18/syu.mat
svm/cj/x19-18/x13-16/x13-16.txt
svm/cj/x19-18/x13-16/x13-16wx.txt
svm/cj/x19-18/x13-16/x13-16(说明).doc
svm/cj/x19-18/x19-18说明.doc
svm/cj/x19-18/新建 Microsoft Word 文档.doc
svm/cj/x22-15.txt
svm/cj/协方差/sys2.mat
svm/cj/协方差/x13+pe.mat
svm/cj/协方差/x13+pe.txt
svm/cj/试验/sh4.mat
svm/cj/试验/shiyan3.mat
svm/cj/试验/shu2.mat
svm/cj/试验/shuju1.mat
svm/cj/试验/sy1.mat
svm/cj/试验/wbiao.mat
svm/cj/试验/修改-试验预测pe.mat
svm/cj/试验/数据(无标准化)1.mat
svm/cj/试验/预测空隙度-实验.mat
svm/cj/说明.doc
svm/cj/预测参透率pe/pemo1.mat
svm/cj/预测参透率pe/sy1.mat
svm/cj/预测参透率pe/sy2.mat
svm/cj/预测参透率pe/sy3.mat
svm/cj/预测参透率pe/SY4.mat
svm/cj/预测参透率pe/syy1.mat
svm/cj/预测参透率pe/tu/t.doc
svm/cj/预测参透率pe/tu/x19-pe-x40.fig
svm/cj/预测参透率pe/TU.doc
svm/cj/预测参透率pe/记录1.doc
svm/cj/预测孔隙度po/matlab.mat
svm/cj/预测孔隙度po/matlabkj.mat
svm/cj/预测孔隙度po/s1.mat
svm/cj/预测孔隙度po/s2.mat
svm/cj/预测孔隙度po/sy3.mat
svm/cj/预测孔隙度po/记录2.doc
svm/cj/预测孔隙度po/预测-修改/matlab.mat
svm/cj/预测孔隙度po/预测-修改/po-xiugai.mat
svm/cj/预测孔隙度po/预测-修改/xiugai2.mat
svm/cj/预测孔隙度po/预测-修改/几路3.doc
svm/cmap.mat
svm/Contents.m
svm/Examples/beifen/welldat/x13date.mat
svm/Examples/beifen/welldat/x38-14date.mat
svm/Examples/bijiao.fig
svm/Examples/Classification/iris1v23.mat
svm/Examples/Classification/iris2v13.mat
svm/Examples/Classification/iris3v12.mat
svm/Examples/Classification/linsep.mat
svm/Examples/Classification/nlinsep.mat
svm/Examples/Classification/zxzxp.mat
svm/Examples/Classification/zzzzz.mat
svm/Examples/date1/matlab.mat
svm/Examples/date1/n1704jiguo.mat
svm/Examples/date1/norm1704.mat
svm/Examples/date1/norm1704all.mat
svm/Examples/date1/norm1704fenkuai.mat
svm/Examples/date1/normh1704.mat
svm/Examples/date1/normi1704.mat
svm/Examples/date1/normo1704.mat
svm/Examples/HUI/desktop.ini
svm/Examples/HUI/hui.mat
svm/Examples/HUI/HUITU.mat
svm/Examples/HUI/函数1.mat
svm/Examples/Regression/desktop.ini
svm/Examples/Regression/example.mat
svm/Examples/Regression/sinc.mat
svm/Examples/Regression/titanium.mat
svm/Examples/Regression/zyz=x.mat
svm/Examples/shiyan1/bf/HUI.mat
svm/Examples/shiyan1/bf/shiyan-tu.doc
svm/Examples/shiyan1/bf/tu.doc
svm/Examples/shiyan1/bf/untitled.fig
svm/Examples/shiyan1/bf/函数1.mat
svm/Examples/shiyan1/bf/试验1.mat
svm/Examples/shiyan1/bijiao2.mat
svm/Examples/shiyan1/desktop.ini
svm/Examples/shiyan1/shiy1.fig
svm/Examples/shiyan1/sin2.fig
svm/Examples/shiyan1/sinnn.fig
svm/Examples/shiyan1/untitled.fig
svm/Examples/shiyan1/Wbijaio.fig
svm/Examples/shiyan1/zyz.fig
svm/Examples/shiyan1/zyz4.fig
svm/Examples/shiyan1/zyz5.fig
svm/Examples/shiyan1/函数1.mat
svm/Examples/shiyan1/记录1.doc
svm/Examples/shiyan2/desktop.ini
svm/Examples/shiyan2/shya2.mat
svm/Examples/shiyan2/zyz.m
svm/Examples/shiyan2/新建 Microsoft Word 文档.doc
svm/Examples/shiyan3/desktop.ini
svm/Examples/shiyan3/shiYZ1.mat
svm/Examples/shiyan3/zyz.m
svm/Examples/shiyan3/函数1.mat
svm/Examples/shiyan3/新建 Microsoft Word 文档.doc
svm/Examples/untitled.fig
svm/Examples/welldat/x13date.mat
svm/Examples/welldat/x38-14date.mat
svm/Examples/welldat/x43-6/x43-6normdate.mat
svm/Examples/welldat/x43-6/x43-6quxiantu.fig
svm/Examples/welldat/x43-6/x43.mat
svm/Examples/welldat/x43-6/记录.doc
svm/Examples/zh.m
svm/Examples/zzzzz.mat
svm/Examples/与样条函数之比较/yangtiao.mat
svm/Examples/与样条函数之比较/样条.doc
svm/Examples/记录1.doc
svm/lagrange.m
svm/newsvm.zip
svm/nobias.m
svm/norm1704all.mat
svm/Optimiser/Makefile
svm/Optimiser/pr_loqo.c
svm/Optimiser/pr_loqo.h
svm/Optimiser/qp.c
svm/Optimiser/qp.dll
svm/qp.dll
svm/README
svm/softmargin.m
svm/svc.m
svm/svcerror.m
svm/svcinfo.m
svm/svcoutput.m
svm/svcplot.m
svm/svdatanorm.m
svm/svkernel.m
svm/svr.m
svm/svrerror.m
svm/svroutput.m
svm/svrplot.m
svm/svtol.m
svm/temp.mat
svm/temp00.m
svm/uiclass.m
svm/uiclass.mat
svm/uiregress.m
svm/uiregress.mat
svm/zhang.asv
svm/zhang.m
svm/zyz.asv
svm/zyz.m
svm/cj/x19-18/x13-16
svm/cj/预测参透率pe/tu
svm/cj/预测孔隙度po/预测-修改
svm/Examples/beifen/welldat
svm/Examples/shiyan1/bf
svm/Examples/welldat/x43-6
svm/cj/x19-18
svm/cj/协方差
svm/cj/试验
svm/cj/预测参透率pe
svm/cj/预测孔隙度po
svm/Examples/beifen
svm/Examples/Classification
svm/Examples/date1
svm/Examples/HUI
svm/Examples/Regression
svm/Examples/shiyan1
svm/Examples/shiyan2
svm/Examples/shiyan3
svm/Examples/welldat
svm/Examples/与样条函数之比较
svm/cj
svm/Examples
svm/Optimiser
svm

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