huanghao
2012年12月2日 11:32
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#1: max geomatric margin, subject to 1) all geo margin of sample greater than that, 2) geo margin equals functional margin #2: different way to pose the same target, no convex st

#1: max geomatric margin, subject to 1) all geo margin of sample greater than that, 2) geo margin equals functional margin #2: different way to pose the same target, no convex st
svm
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自指
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自指
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ppt素材
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ppt素材
2
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sobel
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sobel
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original
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original
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face recognition processing flow
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face recognition processing flow
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final notation: scale the functional margin to 1 and minimize ||w||
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final notation: scale the functional margin to 1 and minimize ||w||
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#1: max geomatric margin, subject to 1) all geo margin of sample greater than that, 2) geo margin equals functional margin #2: different way to pose the same target, no convex st
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#1: max geomatric margin, subject to 1) all geo margin of sample greater than that, 2) geo margin equals functional margin #2: different way to pose the same target, no convex st
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formal definition of max margin classifier
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formal definition of max margin classifier
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geomatric margin and functional margin
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geomatric margin and functional margin
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geomatric margin: project sample point onto separate plane
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geomatric margin: project sample point onto separate plane
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min functional margin of the sample set. and restrict the length of weight equals 1
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min functional margin of the sample set. and restrict the length of weight equals 1
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functional margin: we want it to be large
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functional margin: we want it to be large
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notation of svm
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notation of svm
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eigenface method to solve MINST
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eigenface method to solve MINST
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> correct.ratio [1] 0.9285714
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> correct.ratio [1] 0.9285714
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predict result
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predict result
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eigenfaces
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eigenfaces
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meanface
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meanface
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recognizing
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recognizing
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eigenface is eigenvector of C
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eigenface is eigenvector of C
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calculate eigenvector of L and also A
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calculate eigenvector of L and also A
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calculate L matrix which has extremly lower size, M by M
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calculate L matrix which has extremly lower size, M by M
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covariance of A, huge matrix of N^2 by N2^
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covariance of A, huge matrix of N^2 by N2^
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computer matrix A
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computer matrix A
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+svm
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