Developing and Evaluating an Ensemble of Multiclass SVMs for Honey Botanical Origins Classification using Hyperspectral Imaging Data

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dc.contributor.author Phillips, Tessa en
dc.contributor.author Abdulla, Waleed en
dc.date.accessioned 2019-05-16T23:57:10Z en
dc.date.issued 2019-03-15 en
dc.identifier.uri http://hdl.handle.net/2292/46459 en
dc.description.abstract Support vector machines (SVMs) have been applied to many real world datasets with great success. They are widely considered state of the art methods to solve many problems. However, the leap is when they have been adopted by deep neural networks. SVMs perform very well, and can outperform the end to end neural networks when used in combination with deep neural network extracted features. This is the line we intend to investigate in this research. Different techniques for SVMs are important in order to achieve high classification accuracy, particularly with the high generalisation attributed to SVMs. We propose a novel ensemble technique for classifying multiclass hyperspectral honey dataset. This method uses an ensemble of tree-based multiclass SVMs to try reducing the effects of error propagation down the trees. The results show that this approach is comparable to the commonly used parallel method for multiclass SVM. en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Developing and Evaluating an Ensemble of Multiclass SVMs for Honey Botanical Origins Classification using Hyperspectral Imaging Data en
dc.type Report en
dc.rights.holder Copyright: The author en
pubs.place-of-publication The University of Auckland en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Technical Report en
pubs.elements-id 763697 en
pubs.org-id Engineering en
pubs.org-id Department of Electrical, Computer and Software Engineering en
pubs.org-id Science en
pubs.org-id Physics en
pubs.record-created-at-source-date 2019-02-26 en


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