In the pdf, it said that the optimization problem in SVMs have a nice property in that it was quadratic, which means that there's a nice global minimum to go towards, and not lots of local minimum like in NN. That means, it seems SVMs won't get stuck at a suboptimal solution.
Is that not a problem in DNNs now? Or is it that it's such high dimensionality that local minima don't stop the optimizer, because there's always another way around the local minimum?
In the pdf, it said that the optimization problem in SVMs have a nice property in that it was quadratic, which means that there's a nice global minimum to go towards, and not lots of local minimum like in NN. That means, it seems SVMs won't get stuck at a suboptimal solution.
Is that not a problem in DNNs now? Or is it that it's such high dimensionality that local minima don't stop the optimizer, because there's always another way around the local minimum?