Abstract
The standard SVM classifier is not adjusted to processing large training set as the computational complexity can reach O(n 3). To overcome this limitation we discuss the idea of reducing the size of the training data by initial preprocessing of the training set using Learning Vector Quantization (LVQ) neural network and then building the SVM model using prototypes returned by the LVQ network. As the LVQ network scales linearly with n, and in contrast to clustering algorithms utilizes label information it seems to be a good choice for initial data compression.
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
Asuncion, A., Newman, D.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bottou, L., Lin, C.J.: Support vector machine solvers. In: Large Scale Kernel Machines, pp. 301–320 (2007)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
Garcia, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 417–435 (2012)
Grochowski, M., Jankowski, N.: Comparison of instance selection algorithms II. Results and comments. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 580–585. Springer, Heidelberg (2004)
Herrera, F.: Keel, knowledge extraction based on evolutionary learning, Spanish National Projects TIC2002-04036-C05, TIN2005-08386-C05 and TIN2008-06681-C06 (2005), http://www.keel.es
Jankowski, N., Grochowski, M.: Comparison of instances seletion algorithms I. Algorithms survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004)
Kohonen, T.: Learning vector quantization. In: Self-Organizing Maps, pp. 203–217. Springer (1997)
Kordos, M., Rusiecki, A.: Improving MLP neural network performance by noise reduction. In: Dediu, A.-H., Martín-Vide, C., Truthe, B., Vega-Rodríguez, M.A. (eds.) TPNC 2013. LNCS, vol. 8273, pp. 133–144. Springer, Heidelberg (2013)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Blachnik, M. (2015). Reducing Time Complexity of SVM Model by LVQ Data Compression. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-19324-3_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19323-6
Online ISBN: 978-3-319-19324-3
eBook Packages: Computer ScienceComputer Science (R0)