文件名称:kernels
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- 上传时间：2013-07-16
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文件大小：88.09kb
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根据模式识别理论，低维空间线性不可分的模式通过非线性映射到高维特征空间则可能实现线性可分，但是如果直接采用这种技术在高维空间进行分类或回归，则存在确定非线性映射函数的形式和参数、特征空间维数等问题，而最大的障碍则是在高维特征空间运算时存在的“维数灾难”。采用核函数技术可以有效地解决这样问题。
-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.
-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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下载文件列表
kernels/
kernels/Contents.m
kernels/Contents.m~
kernels/diagker.c
kernels/diagker.dll
kernels/diagker.m
kernels/diagker.mexa64
kernels/diagker.mexglx
kernels/dualcov.m
kernels/dualmean.m
kernels/extraction/
kernels/extraction/Contents.m
kernels/extraction/gda.m
kernels/extraction/greedyappx.m
kernels/extraction/greedyappx.m~
kernels/extraction/greedykpca.m
kernels/extraction/greedykpca.m~
kernels/extraction/kpca.m
kernels/extraction/kpca.m~
kernels/extraction/kpcarec.m
kernels/extraction/kpcarec.m~
kernels/greedykls.m
kernels/kdist.m
kernels/kernel.c
kernels/kernel.dll
kernels/kernel.m
kernels/kernel.mexa64
kernels/kernel.mexglx
kernels/kernel_fun.c
kernels/kernel_fun.c~
kernels/kernel_fun.h
kernels/kernelproj.m
kernels/kernelproj_mex.c
kernels/kernelproj_mex.c~
kernels/kernelproj_mex.dll
kernels/kernelproj_mex.mexa64
kernels/kernelproj_mex.mexglx
kernels/kfd.m
kernels/knorm.m
kernels/knorm.m~
kernels/kperceptr.m
kernels/lin2svm.m
kernels/minball.m
kernels/minball.m~
kernels/preimage/
kernels/preimage/Contents.m
kernels/preimage/rbfpreimg.m
kernels/preimage/rbfpreimg.m~
kernels/preimage/rbfpreimg2.m
kernels/preimage/rbfpreimg3.m
kernels/redquadh.m
kernels/rspoly2.m
kernels/rspoly2.m~
kernels/rsrbf.m
kernels/rsrbf.m~
kernels/Contents.m
kernels/Contents.m~
kernels/diagker.c
kernels/diagker.dll
kernels/diagker.m
kernels/diagker.mexa64
kernels/diagker.mexglx
kernels/dualcov.m
kernels/dualmean.m
kernels/extraction/
kernels/extraction/Contents.m
kernels/extraction/gda.m
kernels/extraction/greedyappx.m
kernels/extraction/greedyappx.m~
kernels/extraction/greedykpca.m
kernels/extraction/greedykpca.m~
kernels/extraction/kpca.m
kernels/extraction/kpca.m~
kernels/extraction/kpcarec.m
kernels/extraction/kpcarec.m~
kernels/greedykls.m
kernels/kdist.m
kernels/kernel.c
kernels/kernel.dll
kernels/kernel.m
kernels/kernel.mexa64
kernels/kernel.mexglx
kernels/kernel_fun.c
kernels/kernel_fun.c~
kernels/kernel_fun.h
kernels/kernelproj.m
kernels/kernelproj_mex.c
kernels/kernelproj_mex.c~
kernels/kernelproj_mex.dll
kernels/kernelproj_mex.mexa64
kernels/kernelproj_mex.mexglx
kernels/kfd.m
kernels/knorm.m
kernels/knorm.m~
kernels/kperceptr.m
kernels/lin2svm.m
kernels/minball.m
kernels/minball.m~
kernels/preimage/
kernels/preimage/Contents.m
kernels/preimage/rbfpreimg.m
kernels/preimage/rbfpreimg.m~
kernels/preimage/rbfpreimg2.m
kernels/preimage/rbfpreimg3.m
kernels/redquadh.m
kernels/rspoly2.m
kernels/rspoly2.m~
kernels/rsrbf.m
kernels/rsrbf.m~
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