Comment by Dr_Birdbrain
3 years ago
An SVM is a quadratic program, which is convex. This means that they should always converge and they should always converge to the same global optimum, regardless of initialization, as long as they are feasible, I.e. as long as the two classes can be separated by an SVM.
The soft-margin SVM which can handle misclassifications is also convex and has a unique global optimum [0].
[0] https://stackoverflow.com/a/12610455/992102
> as long as the two classes can be separated by an SVM.
Are the classes separable with e.g. the intertwined spiral dataset in the TensorFlow demo? Maybe only with a radial basis function kernel?
Separable state https://news.ycombinator.com/item?id=37369783