SVMClass

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

Function:

Description:

Signature:

Input Arguments:

Name:Optional:Description:
Samples No The N M-dimensional input patterns to classify (MxN matrix).
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).

Output Arguments:

Name:Optional:Description:
Labels No The predicted labels computed by taking the signum of the decision value (1xN) matrix.
DecisionValue No The output of the non-thresholded decision function (1xN matrix).

See Also:


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