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Extreme Kernel Machine
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The purpose of this report is to examine the combination of an Extreme Learning Machine (ELM) with the Kernel Method

. Kernels lies at the core of Support Vector Machines success in classifying non-linearly separable datasets. The hypothesis is that by combining ELM with a kernel we will utilize features in the ELM-space otherwise unused. The report is intended as a proof of concept for the idea of using kernel methods in an ELM setting. This will be done by running the new algorithm against five image datasets for a classification accuracy and time complexity analysis. Results show that our extended ELM algorithm, which we have named

Extreme Kernel Machine (EKM), improve classification accuracy for some datasets compared to the regularised ELM, in the best scenarios around three percentage points. We found that the choice of kernel type and parameter values had great effect on the classification performance. The implementation of the kernel does however add computational complexity, but where that is not a concern EKM does have an advantage. This tradeoff might give EKM a place between other neural networks and regular ELMs.

Place, publisher, year, edition, pages
2017. , 10 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-211566OAI: oai:DiVA.org:kth-211566DiVA: diva2:1130092
Examiners
Available from: 2017-08-08 Created: 2017-08-08 Last updated: 2017-08-08Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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