The supervised algorithm searches for a function that takes values
from Rm and goes to Y, here {0,1} being the two possible values. The
basic approach to choose the function is a structural risk minimization.
This implies to seek the best fitting function for the training data taking
into account a term to control bias/variance trade-off.