Abstract
To solve multi-class classification problems for large-scale datasets, the authors propose a coded output support vector machine (COSVM) by introducing the idea of information coding. The COSVM is built based on the support vector regression (SVR) machine that is implemented by the sequential minimal optimization (SMO) algorithm. The paper first introduces the soft ε-tube SVR’s basic principles, next gives the idea and procedure of the SMO algorithm, and then illustrates the COSVM’s topology. For studying the parameters impact on the binary COSVM’s performance, we perform two experiments with the Character Trajectories dataset, in which output labels are coded with the binary number system. And some useful results are obtained in these experiments. The final section gives a conclusion and further research ideas.
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
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Haussler, D. (ed.) 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152. ACM Press, Pittsburgh (1992)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Cortes, C., Vapnik, V.N.: Support vector networks. Machine Learning 20, 273–297 (1995)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Deng, N.Y., Tian, Y.J.: A New Method in Data Mining: Support Vector Machine. China Science Press, Beijing (2004) (in Chinese)
Dietterich, T.G., Bakiri, G.: Solving multi-class learning problems via error-correcting output codes. Journal of Artificial Intelligent Research 2, 263–286 (1995)
Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)
Newman, D.J., Hettich, S., Blake, C.L., et al.: UCI Repository of machine learning databases. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. In: Principe, J., Gile, L., Morgan, N. (eds.) Neural Networks for Signal Processing VII - The 1997 IEEE Workshop, pp. 276–285. IEEE Press, Piscataway (1997)
Platt, J.C.: Sequential minimal optimization: A fast algorithm for training support vector machines. Technical report, MSR-TR-98-14, Microsoft Research (1998)
Schölkopf, B., Smola, A.J., Williamson, R., et al.: New support vector algorithms. Neural Computation 12, 1207–1245 (2000)
Smola, A.J., Schölkopf, B., Müller, K.R.: General cost functions for support vector regression. In: Downs, T., Frean, M., Gallagher, M. (eds.) 9th Australian Conference on Neural Network, pp. 79–83. University of Queensland, Brisbane (1998)
Smola, A.J.: Learning with Kernels. PhD dissertation. Technische Universität Berlin, Berlin (1998)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, New York (1998)
Vapnik, V.N.: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5), 988–999 (1999)
Vapnik, V.N., Chervonenkis, A.: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications 16(2), 264–280 (1971)
Ye, T., Zhu, X.F.: The bridge relating process neural networks and traditional neural networks. Neurocomputing 74(6), 906–915 (2011)
Zhu, Y.P., Dai, R.W.: Text classifier based on SVM decision tree. Pattern Recognition & Artificial Intelligence 18(4), 412–416 (2005) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ye, T., Zhu, X. (2013). Binary Coded Output Support Vector Machine. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-39482-9_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39481-2
Online ISBN: 978-3-642-39482-9
eBook Packages: Computer ScienceComputer Science (R0)