I am studying SVM from Andrew ng machine learning notes. I don't fully understand the optimization problem for svm that is stated in the notes. So we have optimization problem

$$\max_{\gamma, w, b}\gamma$$ s.t. : $$y^{(i)}(w^Tx^{(i)}+b)\geq\gamma, i=1,\dots m,\\||w||=1.$$

I get a little bit confused here, as i don't see why we should need this first constraint. Isn't it enough only to maximize $\gamma$ and in that case we already have the maximal margin hyperplane where observations are at least $\gamma$ away from the decision boundry (because $\gamma$ is already defined this way)?

I think I don't understand something simple here. If you have any explanation for this I would appreciate it very much.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.