| 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). |
| 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 parameters to control training (1xa row vector, a<=11). This row vector must consist of the following elements. - (1) Kernel Type: (default=2).
- (0) Linear
- (1) Polynomial: Gamma*(<X(:,i),X(:,j)>+Coefficient)^Degree
- (2) RBF: (exp(-Gamma*|X(:,i)-X(:,j)|^2))
- (3) Sigmoid: tanh(Gamma*<X(:,i),X(:,j)>+Coefficient)
- (2) Degree: (default=3).
- (3) Gamma: If the input value is zero, Gamma will be set to 1.0 /(max_pattern_dimension). Otherwise, Gamma will remain unchanged in the function (default=1).
- (4) Coefficient: (default=0).
- (5) C: Cost of constraint violation for C-SVC, epsilon-SVR, and nu-SVR (default=1).
- (6) Cache: Space to hold the elements of K(<X(:,i),X(:,j)>) matrix (default=40MB).
- (7) epsilon: tolerance of termination criterion (default=0.001).
- (8) SVM Type (default=0)
- (0) C-SVC
- (1) nu-SVC
- (2) one-class SVM
- (3) epsilon-SVR
- (4) nu-SVR
- (9) nu: nu of nu-SVC, one-class SVM, and nu-SVR (default=0.5).
- (10) loss tolerance: epsilon in loss function of epsilon-SVR (default=0.1).
- (11) shrinking: use shrinking heuristics. (default=1<yes>).
|
| nSV |
Yes |
The numbers of SVs for each class (1xL row vector). |
| nLabel |
Yes |
The labels of each class (1xL row vector). |
| Weight |
Yes |
Used to weight classes. This is acheived by multiplying the C for a class i by weight(i) in C-SVC (default=all 1.0 's). |