| Package | Description |
|---|---|
| it.uniroma2.sag.kelp.learningalgorithm.classification.libsvm.solver |
| Modifier and Type | Method and Description |
|---|---|
SvmSolution |
LibSvmSolver.solve(int l_,
Dataset dataset,
float[] p_,
int[] y_,
float[] initial_alpha)
It solves the SMO algorithm in [CC Chang & CJ Lin, 2011]
min 0.5(\alpha^T Q \alpha) + p^T \alpha
y^T \alpha = \delta
y_i = +1 or -1 0 <= alpha_i <= Cp for y_i = 1 0 <= alpha_i <= Cn for y_i = -1 Given: Q, p, y, Cp, Cn, and an initial feasible point \alpha l is the size of vectors and matrices eps is the stopping tolerance solution will be put in \alpha, objective value will be put in obj |
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