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pro vyhledávání: '"Björling, Axel"'
Autor:
Björling, Axel
This report explores whether machine learning methods such as regression and classification can be used with the goal of estimating the resolution time of trouble tickets in a telecommunications network. Historical trouble ticket data from Telenor we
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-313291
Autor:
Björling, Axel, Hillesöy, Fredrik
This report is a part of the project Pre-study on a proactive and integrative cycling strategy for KTH. A survey about the travel habits of KTH students to and from campus was done in collaboration with Nils Brown, employee at the SEED-institution at
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231626
Autor:
Björling, Axel
This report explores whether machine learning methods such as regression and classification can be used with the goal of estimating the resolution time of trouble tickets in a telecommunications network. Historical trouble ticket data from Telenor we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::26ad658f3755b60ba5892a623357d1c1
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-313291
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-313291
Autor:
Björling, Axel, Hillesöy, Fredrik
This report is a part of the project Pre-study on a proactive and integrative cycling strategy for KTH. A survey about the travel habits of KTH students to and from campus was done in collaboration with Nils Brown, employee at the SEED-institution at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::5a5f28c58653b379dcfc363b5899acae
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231626
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231626