Abstract
This paper presents the comparableness of SVM method to artificial neural networks in the outlier detection problem of high dimensions. Experiments performed on real dataset show that the performance of this method is mostly superior to that of artificial neural networks. The proposed method, SVM served to exemplify that kernel-based learning algorithms can be employed as an efficient method for evaluating the revegetation potentiality of abandoned lands from coal mining activities.
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
Preview
Unable to display preview. Download preview PDF.
References
Ying, W., Daoliang, L.: A potentiality evaluation model for revegetation of abandoned lands from coal mining activities. Journal of China Agricultural University 10, 88–92 (2005)
Zhang, Z.Q., Wong, M.H., Nie, X.P., Lan, C.Y.: Effects of zinc (zinc sulfate) on rhizobia-earleaf acacia (acacia auriculaeformis) symbiotic association. Bioresource Technology 64, 97–104 (1998)
Neel, C., Bril, H., Courtin-Nomade, A.: Factors affecting natural development of soil on 35-year-old sulphide-rich mine tailings. Geoderma 111, 1–20 (2003)
Yang, B.-S., Hwang, W.-W., Kim, D.-J., Tan, A.C.: Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines. Mechanical Systems and Signal Processing 19, 371–390 (2005)
Shina, H.J., Eom, D.-H., Kim, S.-S.: One-class support vector machines—an application in machine fault detection and classification. Computers & Industrial Engineering 48, 395–408 (2005)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: An application to face detection. In: 1997 Conference on computer vision and pattern recognition, pp. 130–136. IEEE, Puerto Rico (1997)
Jack, L.B., Nandi, A.K.: Fault detection using support vector machines and artificial neural networks. Augmented by genetic algorithms.Mechanical Systems and Signal Processing 16(2–3), 373–390 (2002)
Vapnik, V.N.: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5), 988–999 (1999)
Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithm. IEEE Transactions on Neural Network 12(2), 181–201 (2001)
Song, J., Tang, H.: Support vector machines for classification of homo-oligomeric proteins by incorporating subsequence distributions. Journal of Molecular Structure: THEOCHEM 722, 97–101 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhuang, C., Fu, Z., Yang, P., Zhang, X. (2005). The Application of Support Vector Machine in the Potentiality Evaluation for Revegetation of Abandoned Lands from Coal Mining Activities. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_88
Download citation
DOI: https://doi.org/10.1007/11596448_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
eBook Packages: Computer ScienceComputer Science (R0)