The wikipedia link is to the closest concept I could find, but you're right it's not a perfect match. If there's anything better...
On the more general point, I'm basically trying to dispel the myth that "cool" machine learning people do like neural nets and decision trees (which must thus be cool) while "boring" statisticians do stuff like regression and discriminant analysis (which must thus be boring). You can find good, interesting papers by people from both departments working on all kinds of techniques. (I do have a vested interest given I'm looking at regression at the moment.) For example, which class does a graphical model belong to? It really seems to me to be equally studied in "machine learning labs" and "statistics departments".
I still think there's not a **fundamental** distinction in the kind of things being done between the two.