Text Classification with Cellular Automata Networks

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dc.contributor.authorJohansson, Oscar
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mikroteknologi och nanovetenskap (MC2)sv
dc.contributor.departmentChalmers University of Technology / Department of Microtechnology and Nanoscience (MC2)en
dc.contributor.examinerKonkoli, Zoran
dc.contributor.supervisorKonkoli, Zoran
dc.date.accessioned2024-07-01T10:33:29Z
dc.date.available2024-07-01T10:33:29Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractThe 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.
dc.identifier.coursecodeMCCX04
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308170
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectReservoir computing, cellular automata networks, cellular automata, text classification, machine learning, natural language processing, supervised learning, information theory, elementary cellular automata
dc.titleText Classification with Cellular Automata Networks
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeData science and AI (MPDSC), MSc
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