Training , Validation , Test set , A more general case

A regular practice is dividing training data into tree parts to evaluate the performance of the algorithm. Training set is used to train  parameters of the model. Validation set is used to "train" hyper parameters of the model and test set is used to evaluate the model.
     For more general sense what if I divide my training set into n part and use first set to bottom level parameters  for the tree (One can use topological sort in graphical model) of the parameters. Than iterate next upper level training upper level parameters in next set. At the end we would have no parameters above the top which corresponds to a test set. Basically , make the distinction between parameters and hyper parameters fuzzy.