PolySVC

Function | Description | Signature | Input Arguments | Output Arguments | See Also

Function:

Description:

Signature:

Input Arguments:
Name:Optional:Description:
Samples No The N input patterns in M dimensions (MxN matrix, a row vector of columns).
Labels No The labels of the N input patterns (1xN row vector).
Degree Yes The Degree parameter of the polynomial kernel, which has the form of (Gamma*<X(:,i),X(:,j)>+Coefficient)^Degree.
C Yes Cost of constraint violation for C-SVC, epsilon-SVR, and nu-SVR (default=1).
Gamma Yes The Gamma parameter of the polynomial kernel, which has the form of (Gamma*<X(:,i),X(:,j)>+Coefficient)^Degree.
Coeff Yes The Coeff parameter of the polynomial kernel, which has the form of (Gamma*<X(:,i),X(:,j)>+Coefficient)^Degree.

Output Arguments:
Name:Optional:Description:
AlphaY No Alpha * Y where the Alpha vector contains the non-zero Lagrange Coefficients and Y are the corresponding labels.
  • Binary Classification/Outlier Detection: A row vector where AlphaY(i) corresponds to the multiplier of the i'th support vector.
  • Multi-Class Classification: A (L-1) x sum(nSV) matrix. AlphaY(i,j) is the multiplier for a classifier between classes i and (i+1) where i < length(nSV) and corresponds to the j'th support vector.
  • Regression: A row vector where AlphaY(i) corresponds to the i'th support vector.
SVs No The Support Vectors (M x sum(nSV) matrix) for this classifier/regressor-- the samples corresponding to the non-zero Lagrange multipliers AlphaY. A M x sum(nSV) matrix of sum(nSV) support vectors in M dimensions.
  • Binary Classification/Outlier Detection/Multi-class Classification: The class of a support vector i is obtained by: nLabel(min(find(cumsum(nSV)>i))). The columns from u(i) to u(i+1) represent the support vectors with a corresponding class nLabel(i) where u=[1,cumsum(nSV)].
    • [SVs from Class_1, SVs from Class_2, [...], SVs from Class_L];
  • Regression: A M x sum(nSV) matrix of support vectors. Labels do not necessarily dictate the organization of this matrix.
Bias No
  • Binary Classification/Outlier Detection/Regression: A scalar value representing the bias or threshold of the SVM classifier.
  • Multi-class Classification: A (1x(L*(L-1)/2)) row vector of biases for all the two-class classifiers.
Parameters Yes The training parameters used.
nSV No The numbers of SVs for each class (1xL row vector).
nLabel Yes The labels of each class (1xL row vector).

See Also:

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