Prediction of Sewer Condition Grade Using Support Vector Machines-Reference-Cited by-同舟云学术

Prediction of Sewer Condition Grade Using Support Vector Machines

Published:2011-07 Issue:4 Volume:25 Page:283-290
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Mashford John¹²³⁴,Marlow David¹²³⁴,Tran Dung¹²³⁴,May Robert¹²³⁴

Affiliation:

1. Senior Research Scientist, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Division of Land and Water, PO Box 56, Highett, Vic. 3190, Australia (corresponding author).

2. Project Leader, Asset Management, CSIRO Division of Land and Water, PO Box 56, Highett, Vic. 3190, Australia.

3. Postdoctoral Research Fellow, Institute of Sustainability and Innovation, Victoria Univ., PO Box 14428, Melbourne, Vic. 8001, Australia.

4. Senior Research Engineer, Research and Development, United Water International Pty. Ltd., GPO Box 1875 Adelaide, SA 5001, Australia.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000089

Reference32 articles.

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5. Chang C.-C. and Lin C.-J. (2001). “LIBSVM: A library for support vector machines.” 〈http://www.csie.ntu.edu.tw/~cjlin/libsvm〉 (September 18 2009).

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