| 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). |