oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Classifying Forest Cover type with cartographic variables via the Support Vector Machine, Naive Bayes and Random Forest classifiers.
Örebro University, Örebro University School of Business.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Place, publisher, year, edition, pages
2017. , 29 p.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-58384OAI: oai:DiVA.org:oru-58384DiVA: diva2:1117814
Subject / course
Statistik
Supervisors
Examiners
Available from: 2017-06-29 Created: 2017-06-29 Last updated: 2017-06-29Bibliographically approved

Open Access in DiVA

fulltext(686 kB)23 downloads
File information
File name FULLTEXT01.pdfFile size 686 kBChecksum SHA-512
323205370263da519f0066f4da1bc795a07994f8d854fa7ef1fb3705a9d8dfdfb107ccf1a6cb712b605d7084b92a985c0d51264f943df2114c54bfda6b100508
Type fulltextMimetype application/pdf

By organisation
Örebro University School of Business
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 23 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 34 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf