Abstract
While transductive support vector machine (TSVM) utilizes the information carried by the unlabeled samples for classification and acquires better classification performance than support vector machine (SVM), the number of positive samples must be appointed before training and it is not changed during the training phase. In this paper, a sequential minimal transductive support vector machine (SMTSVM) is discussed to overcome the deficiency in TSVM. It solves the problem of estimation the penalty value after changing a temporary label by introducing the sequential minimal way. The experimental results show that SMTSVM is very promising.
Supported by the Shanghai Leading Academic Discipline Project (No. S30405), and the NSF of Shanghai Normal University (No. SK200937).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, Y.S., Wang, G.P., Dong, S.H.: Learning with progressive transductive support vector machine. Patte. Recog. Lett. 24, 1845–1855 (2003)
Christianini, V., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2002)
Dong, B., Cao, C., Lee, S.E.: Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 37, 545–553 (2005)
Joachims, T.: Making Large-Scale SVM Learning Practical. In: Adv. Kernel Methods Vector Learn. MIT Press, Cambridge (1998)
Joachims, T.: Transductive inference for text classification using support vector machines. In: Proc. ICML 1999, 16th Internat. Conf. Mach. Learn, pp. 200–209 (1999)
Joachims, T.: Transductive learning via spectral graph partitioning. In: Proc. Internat. Conf. Mach. Learn. (ICML 2003), pp. 290–297 (2003)
Liu, H., Huang, S.T.: Fuzzy transductive support vector machines for hypertext classification. Internat. J. Uncert., Fuzz. Knowl. Syst. 12(1), 21–36 (2004)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: An application to face detection. In: Proc. IEEE Conf. Comput. Visi. Pattern Recogn., pp. 130–136 (1997)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
Vapnik, V.: The Natural of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Wang, Y., Huang, S.: Training TSVM with the proper number of positive samples. Patte. Recog. Lett. 26, 2187–2194 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peng, X., Wang, Y. (2009). Learning with Sequential Minimal Transductive Support Vector Machine. In: Deng, X., Hopcroft, J.E., Xue, J. (eds) Frontiers in Algorithmics. FAW 2009. Lecture Notes in Computer Science, vol 5598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02270-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-02270-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02269-2
Online ISBN: 978-3-642-02270-8
eBook Packages: Computer ScienceComputer Science (R0)