## Mean-field theory of meta-learning

### Authors:

- Dariusz Plewczyński

### Abstract

We discuss here the mean-field theory for a cellular automata model of meta-learning. 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 the opposite classification outcome can be observed in the system. Therefore, the probability of selecting a 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 built from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents. © 2009 IOP Publishing Ltd.

- Record ID
- WUTe8a1acfc17074852889675abc88ba12f
- Author
- Journal series
- Journal of Statistical Mechanics-Theory and Experiment, ISSN 1742-5468
- Issue year
- 2009
- Vol
- 2009
- ASJC Classification
- ; ;
- DOI
- DOI:10.1088/1742-5468/2009/11/P11003 Opening in a new tab
- Language
- (en) English
- Score (nominal)
- 0
- Score source
- journalList
- Publication indicators
- = 5; = 4; : 2009 = 1.053; : 2009 = 2.670 (2) - 2009=2.782 (5)

- Uniform Resource Identifier
- https://repo.pw.edu.pl/info/article/WUTe8a1acfc17074852889675abc88ba12f/

- URN
`urn:pw-repo:WUTe8a1acfc17074852889675abc88ba12f`

* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.