"SVM question"

evgeny

given a binary label (e.g. Y/N), with some attributes, i train an SVM model to come up with predictions and corresponding confidence percentages for Y, say.

qn: is there a way to augment (iteratively?) the model such that the resulting confidence percentages are, say, at least x%.

i understand that this may not be possible for all the data points, my question is more if there is a way of feeding this additional criterion directly in to the optimisation?

land

Hi,
that's exactly what the optimization does anyway. There's no need for changing the optimization function.

If you want to vary the parameter settings of the learner, you might use a performance measure. that uses the resulting confidences for estimating the performance and then use a parameter optimization operator.

Greetings,
  Sebastian