Abstract
Support Vector Machine (SVM) is an advanced algorithm and widely used in many applications. In this chapter, Support Vector Machines (SVMs) are described.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boswell D (2002) Introduction to support vector machines
Chen KY (2007) Forecasting systems reliability based on support vector regression with genetic algorithms. Reliab Eng Syst Saf 92(4):423–432
Fletcher T (2009) Support vector machines explained. http://www.tristanfletcher.co.uk/SVM%20Explained.pdf
Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4):725–749
Lindsay D (2003) Matlab workshop 2 an introduction to support vector machine implementations in matlab. Available from: http://www.robots.ox.ac.uk/~az/lectures/ml/matlab2.pdf
Pai P-F (2006) System reliability forecasting by support vector machines with genetic algorithms. Math Comput Model 43(3–4):262–274
Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press
Theodoridis S, Pikrakis A, Koutroumbas K, Cavouras D (2010) Introduction to pattern recognition: a Matlab approach. Academic Press
Venturad (2009) Svm example. http://axon.cs.byu.edu/Dan/678/miscellaneous/SVM.example.pdf
Watanachaturaporn P, Varshney PK, Arora MK (2005) Multisource fusion for land cover classification using support vector machines. 2005 8th International Conference on Information Fusion
Wei Z, Tao T, Ding ZS, Zio E (2013) A dynamic particle filter-support vector regression method for reliability prediction. Reliab Eng Syst Saf 119:109–116
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 Higher Education Press and Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Cao, C., Xu, M., Kamsing, P., Boonprong, S., Yomwan, P., Saokarn, A. (2021). Flooding Identification by Support Vector Machine. In: Environmental Remote Sensing in Flooding Areas. Springer, Singapore. https://doi.org/10.1007/978-981-15-8202-8_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-8202-8_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8201-1
Online ISBN: 978-981-15-8202-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)