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Title:

Heavy Equipment Demand Prediction with Support Vector Machine Regression Towards a Strategic Equipment Management

Document type:
Zeitschriftenaufsatz
Author(s):
Kargul, A.; Glaese, A.; Kessler, S.; Günthner, W.A.
Non-TUM Co-author(s):
nein
Cooperation:
international
Intellectual Contribution:
Contribution to Practice
Journal title:
International Journal of Structural and Civil Engineering Research
Journal listet in FT50 ranking:
nein
Year:
2017
Journal volume:
6
Journal issue:
2
Pages contribution:
137-143
Covered by:
Scopus; Web of Science
Fulltext / DOI:
doi:10.18178/ijscer.6.2.137-143
Print-ISSN:
2319-6009
Judgement review:
0
Key publication:
Nein
Peer reviewed:
Nein
Technology:
Nein
Interdisciplinarity:
Nein
Mission statement:
;
 BibTeX
Details:

826_export.pdf