SVM with quadratic loss I've seen some statement where I got the impression that SVM with a quadratic loss is no more than having a kernel matrix where a multiple of the unit matrix is subtracted from the kernel. It was shown that in the dual problem the L2 loss constant can be merged into the kernel. But it wasn't clear to me. So the question:
Are L1 and L2 loss for SVM substantially different? (Is L2 loss for SVM convex?)
Or does L2 loss correspond to L1 loss with a shifted kernel?
Or anything else?
 A: They are different specially in the case of regression. The effect of the quadratic loss is to average errors (so it is more sensitive to outliers). To see this, if you minimize,
$$
L_{2}(y) = \sum_{i}(y-x_{i})^{2}
$$
you get the mean value. The effect of the L1 loss function is to , so that it leads to sparse solutions robust against outliers. Again, consider,
$$
L_{1}(y) = \sum_{i}|y-x_{i}|
$$
The derivative gives you $\sum_{i}\operatorname{sign}(y-x_{i}) = 0$, which is true for the median. And the median is robust against outliers.
Now, how does this translate for SVMs?
The loss function affects how the kernel function is regularized (i.e. how you compare samples). This is specially critical in the case of regression, since when using the $L_{2}$ the solution is no longer sparse! (which is one of the aspects of SVMs which makes them interesting in practice).
The optimization problem in that case reads,
$$
\min_{w} \sum_{i=1}^{l} \xi_{i}^{2} \\
\text{subject to } y_{i}-<w,\phi(x_{i})> = \xi_{i} \\
||w|| \leq B \text{ and } i = 1,...,l
$$
After deriving with respect to the primal variables and substituting, you get,
$$
\min_{\alpha} -\lambda \sum_{i=1}^{l}\alpha_{i}^{2}-\sum_{i,j}\alpha_{i}\alpha_{j}\kappa(x_{i},x_{j}) + 2\sum_{i}\alpha_{i}y_{i}
$$
for a detailed derivation see for example here.
Notice that the first two terms can be grouped in we define as,
$$
\min_{\alpha} -\sum_{i,j}\alpha_{i}\alpha_{j}\hat{\kappa}(x_{i},x_{j}) + 2\sum_{i}\alpha_{i}y_{i}
$$
where $\hat{\kappa} = (\kappa + \lambda I)$. Notice that this forces the $\alpha$'s not to be sparse anymore.
A: "In the dual problem the L2 loss constant can be merged into the kernel."
It can be done in the hinge loss too. You only need to rephrase the formulation. 
It is done here, for instance:
A nested heuristic for parameter tuning in Support Vector Machines
E Carrizosa, B Martín-Barragán, D Romero Morales
Computers & Operations Research 43, 328-334
