On Wed, Nov 16, 2011 at 11:38, Andreas Müller <[email protected]> wrote: > >>> - a class for regression and one for classification >>> - MSE and cross entropy (for classification only) loss functions >> We need several loss functions and there gradient in cython (we cannot >> reuse the loss function from the SGD module of since the output of a >> MLP can be a multi-variate). For classification we will need hnigeloss >> and squared hingeloss (and hubert for regression). See the source of >> libsgd for a list of useful loss function. >> >> > Can you explain how hinge-loss works for multiple classes? > Or would you train a separate mlp for each class?
Usually the multiclass hinge loss minimizes max(0, 1 + max_(c != correct_class)(score(c, x)) - score(correct class, x)). That is, the correct class must have score 1 higher than any other classes (or, equivalently, than the highest-scoring of all other classes). -- - Alexandre ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
