Abstract
The more the dimensions of a feature space, the more is the computing power needed to classify. Support vector machines (SVMs) main advantages are (1) their effectiveness in a high-dimensional space and in cases where the number of dimensions is higher than the number of instances in the dataset, and (2) their low use of memory and hence their memory efficiency.
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
S. Osowski, K. Siwek, T. Markiewicz, MLP and SVM networks—a comparative study, in Proceedings of the 6th Nordic Signal Processing Symposium, 2004. NORSIG 2004, 11-11 June 2004, pp. 37–40 (2004).
V.R. Jakkula, Tutorial on support vector machine (SVM). Sch. EECS Wash. State Uni. 37(2.5), 3 (2011). [Online]. Available: https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf.
S. Huang, N. Cai, P.P. Pacheco, S. Narrandes, Y. Wang, W. Xu, Applications of support vector machine (SVM) learning in cancer genomics (in eng). Cancer Genomics Proteomics 15(1), 41–51 (2018). https://doi.org/10.21873/cgp.20063
V.N. Vapnik, Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774–780 (1963)
R. Gandhi, Support vector machine—introduction to machine learning algorithms. Medium (2022). https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47. Accessed 11 Mar 2022.
A. Ng, Part V, Support Vector Machines. See stanford.edu (2022). https://see.stanford.edu/materials/aimlcs229/cs229-notes3.pdf. Accessed 14 Mar 2022.
W. Zouhri, L. Homri, J.-Y. Dantan, Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis. Expert Syst. Appl. 189, 115691 (2022). https://doi.org/10.1016/j.eswa.2021.115691
R. Berwick, An idiot’s guide to support vector machines (SVMs). Village Idiot (2022). https://web.mit.edu/6.034/wwwbob/svm.pdf. Accessed 14 Mar 2022
A. Jana, Support vector machines for beginners—Linear SVM—A developer diary. A Developer Diary (2022). http://www.adeveloperdiary.com/data-science/machine-learning/support-vector-machines-for-beginners-linear-svm/. Accessed 11 Mar 2022.
K. Jain, What is support vector machine? Medium (2022). https://towardsdatascience.com/what-is-support-vector-machine-870a0171e690. Accessed 14 Mar 2022.
F. Rossi, N. Villa, Support vector machine for functional data classification. Neurocomputing 69(7), 730–742 (2006). https://doi.org/10.1016/j.neucom.2005.12.010
E. Osuna, R. Freund, F. Girosi, An improved training algorithm for support vector machines, in Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, 24–26 Sept. 1997, pp. 276–285 (1997). https://doi.org/10.1109/NNSP.1997.622408.
N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge University Press, Cambridge, 2000)
A.J. Smola, Regression estimation with support vector learning machines, Technische Universität München, München (1996). [Online]. Available: http://alex.smola.org/papers/1996/Smola96.pdf
C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
M.E. Cholette, P. Borghesani, E.D. Gialleonardo, F. Braghin, Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications. Expert Syst. Appl. 81(C), 39–52 (2017). https://doi.org/10.1016/j.eswa.2017.03.050
L.M.R. Baccarini, V.V. Rocha e Silva, B.R. de Menezes, W.M. Caminhas, SVM practical industrial application for mechanical faults diagnostic. Expert Syst. Appl. 38(6), 6980–6984 (2011). https://doi.org/10.1016/j.eswa.2010.12.017
Z. Wei, Y. Feng, Z. Hong, R. Qu, J. Tan, Product quality improvement method in manufacturing process based on kernel optimisation algorithm. Int. J. Prod. Res. 55(19), 5597–5608 (2017). https://doi.org/10.1080/00207543.2017.1324223
H. Rostami, J.-Y. Dantan, L. Homri, Review of data mining applications for quality assessment in manufacturing industry: support vector machines. Int. J. Metrol. Qual. Eng. 6(4), 401 (2015). [Online]. Available: https://doi.org/10.1051/ijmqe/2015023.
C.J.C. Burges, B. Schölkopf, A.J. Smola, Advances in Kernel Methods: Support Vector Learning (MIT Press, Cambridge, MA, 1999)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
El Morr, C., Jammal, M., Ali-Hassan, H., El-Hallak, W. (2022). Support Vector Machine. In: Machine Learning for Practical Decision Making. International Series in Operations Research & Management Science, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-031-16990-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-16990-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16989-2
Online ISBN: 978-3-031-16990-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)