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Efficient learning algorithms for classification of data by a nonlinear decision boundary

Resource type
Thesis type
(Project) M.Sc.
Date created
2022-08-24
Authors/Contributors
Abstract
In this thesis, we study the classification of data by a nonlinear classification boundary in the case the decision boundary is determined by the inverse of a given function. We propose and investigate efficient machine learning algorithms based on the Gabriel Edited Set, which is an approximate decision-boundary consistent reduced set. The algorithms attempts to account for the geometry of the equivalence classes determined by the inverse of the function. We numerically investigate the behaviour and accuracy of the algorithms on multiple examples.
Document
Extent
45 pages.
Identifier
etd22135
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Estep, Donald
Language
English
Download file Size
etd22135.pdf 1.54 MB

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