Description: According to the pattern recognition theory, a low dimensional space linearly inseparable pattern through nonlinear mapping into a high-dimensional feature space may realize linearly separable, but if directly using this technique for classification or regression in high dimensional space, then there exists nonlinear mapping function form and parameters, the dimension of the feature space and other issues, and the biggest obstacle is the curse of dimensionality in high dimensional feature space. "". The kernel technology can effectively solve this problem.
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kernels
.......\Contents.m
.......\Contents.m~
.......\diagker.c
.......\diagker.dll
.......\diagker.m
.......\diagker.mexa64
.......\diagker.mexglx
.......\dualcov.m
.......\dualmean.m
.......\extraction
.......\..........\Contents.m
.......\..........\gda.m
.......\..........\greedyappx.m
.......\..........\greedyappx.m~
.......\..........\greedykpca.m
.......\..........\greedykpca.m~
.......\..........\kpca.m
.......\..........\kpca.m~
.......\..........\kpcarec.m
.......\..........\kpcarec.m~
.......\greedykls.m
.......\kdist.m
.......\kernel.c
.......\kernel.dll
.......\kernel.m
.......\kernel.mexa64
.......\kernel.mexglx
.......\kernel_fun.c
.......\kernel_fun.c~
.......\kernel_fun.h
.......\kernelproj.m
.......\kernelproj_mex.c
.......\kernelproj_mex.c~
.......\kernelproj_mex.dll
.......\kernelproj_mex.mexa64
.......\kernelproj_mex.mexglx
.......\kfd.m
.......\knorm.m
.......\knorm.m~
.......\kperceptr.m
.......\lin2svm.m
.......\minball.m
.......\minball.m~
.......\preimage
.......\........\Contents.m
.......\........\rbfpreimg.m
.......\........\rbfpreimg.m~
.......\........\rbfpreimg2.m
.......\........\rbfpreimg3.m
.......\redquadh.m
.......\rspoly2.m
.......\rspoly2.m~
.......\rsrbf.m
.......\rsrbf.m~
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