We discuss here the mean-field theory for a cellular automata model of meta-learning.
The meta-learning is the process of combining outcomes of individual learning procedures
in order to determine the final decision with higher accuracy than any single learning
method. Our method is constructed from an ensemble of interacting, learning agents,
that acquire and process incoming information using various types, or different versions
of machine learning algorithms. The abstract learning space, where all agents are
located, is constructed here using a fully connected model that couples all agents
with random strength values. The cellular automata network simulates the higher level
integration of information acquired from the independent learning trials. The final
classification of incoming input data is therefore defined as the stationary state
of the meta-learning system using simple majority rule, yet the minority clusters
that share opposite classification outcome can be observed in the system. Therefore,
the probability of selecting proper class for a given input data, can be estimated
even without the prior knowledge of its affiliation. The fuzzy logic can be easily
introduced into the system, even if learning agents are build from simple binary classification
machine learning algorithms by calculating the percentage of agreeing agents.