Multinomial logistic regression

* Implement multinomial logistic regression
- version of loss and model functions expect (class-1) parameter vectors (see, e.g., [1])
- add tests

* Structural changes:
- introduce abstract multinomial loss
- add regression_function in addition to model: model takes care of the
  type of the model (ordinal, binomial, multinomial) and provides
appropriate gradients and values, and predictions, whereas
regression_function allows the distinction between, e.g., kernelized and
linear models.
- renaming w to theta

* Disabled Mercer map for focus

[1]
http://en.wikipedia.org/wiki/Multinomial_logistic_regression#As_a_set_of_independent_binary_regressions