Text Classification with Cellular Automata Networks
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2024
Författare
Johansson, Oscar
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The thesis focuses on several options of exploiting reservoir computing ideas in the
context of text classification, using cellular automata networks as the reservoir. The
key innovative aspect of the study is related to finding options for computing with
low-density networks, which should be easier to manufacture. The overarching idea is
to extend Wolfram’s elementary cellular automata rules and implement them within
the framework of random cellular automata networks. The primary objectives of this
study are as follows: (i) To determine the optimal network topologies, (ii) to identify
the most effective transition rules within these networks, and (iii) to develop a
robust methodological framework for finding the best networks. By exploring various
network configurations, the aim is to uncover the structural characteristics that
facilitate efficient information propagation and decision-making within the cellular
automata framework. Observations from the study indicate that there exist cellular
automata network configurations which can function as efficient reservoirs. Moreover,
all considered cellular automata networks are categorised into different groups based
on their performance, providing a solid foundation for further research.
Beskrivning
Ämne/nyckelord
Reservoir computing, cellular automata networks, cellular automata, text classification, machine learning, natural language processing, supervised learning, information theory, elementary cellular automata