what is the principle for training a support vector classifier? What is the principle for training a support vector classifier? To have a decision boundary that stays as far away from the closest training instances as possible To fit all training instances to their correct classes To have a non-linear decision boundary To have a decision boundary that stays as close to the farthest training instances as possible