文件名称:titanium
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VC Support Vector Classification
Usage: [nsv alpha bias] = svc(X,Y,ker,C)
Parameters: X - Training inputs
Y - Training targets
ker - kernel function
C - upper bound (non-separable case)
nsv - number of support vectors
alpha - Lagrange Multipliers
b0 - bias term
-VC Support Vector Classification
Usage: [nsv alpha bias] = svc(X,Y,ker,C)
Parameters: X - Training inputs
Y - Training targets
ker - kernel function
C - upper bound (non-separable case)
nsv - number of support vectors
alpha - Lagrange Multipliers
b0 - bias term
Usage: [nsv alpha bias] = svc(X,Y,ker,C)
Parameters: X - Training inputs
Y - Training targets
ker - kernel function
C - upper bound (non-separable case)
nsv - number of support vectors
alpha - Lagrange Multipliers
b0 - bias term
-VC Support Vector Classification
Usage: [nsv alpha bias] = svc(X,Y,ker,C)
Parameters: X - Training inputs
Y - Training targets
ker - kernel function
C - upper bound (non-separable case)
nsv - number of support vectors
alpha - Lagrange Multipliers
b0 - bias term
相关搜索： SVC
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titanium.mat