An Idiot’s guide to Support vector machines (2003) [pdf]
I have a question!<p>In the pdf, it said that the optimization problem in SVMs have a nice property in that it was quadratic, which means that there&#x27;s a nice global minimum to go towards, and not lots of local minimum like in NN. That means, it seems SVMs won&#x27;t get stuck at a suboptimal solution.<p>Is that not a problem in DNNs now? Or is it that it&#x27;s such high dimensionality that local minima don&#x27;t stop the optimizer, because there&#x27;s always another way around the local minimum?