  There is no free lunch, no single learning algorithm that will outperform
other algorithms on all data. In practice different approaches are tried and
the best algorithm selected. An alternative solution is to build new algorithms
on demand by creating a framework that accommodates many algorithms. The best
combination of parameters and procedures is searched here in the space of all
possible models belonging to the framework of Similarity-Based Methods (SBMs).
Such meta-learning approach gives a chance to find the best method in all
cases. Issues related to the meta-learning and first tests of this approach are
presented.
