Abstract
An hybrid SVM-symbolic architecture for classification tasks is proposed in this work. The learning system relies on a support vector machine (SVM), meanwhile a rule extraction module translate the embedded knowledge in the trained SVM in the form of symbolic rules. The new representation is useful to understand the nature of the problem and its solution. Moreover, a rule insertion module in the hybrid architecture allows incorporate the available prior domain knowledge into the machine expressed in the form of symbolic rules. Thus, it is render possible the integration of SVMs with symbolic AI systems.
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
Andrews, R., Diederich, J., Tickle, A.: A Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks. Knowledge-Based Systems, 8(6) (1995)373–389.
Brailovsky, V., Barzilay, O., Shahave, R.: On global, local, mixed and neighborhood kernels for support vector machines. Pattern Recognition Letters, 20 (1999) 1183–1190
Cortes, C, Vapnik, V.: Support-Vector Networks. Machine Learning, Kluwer Academic Publisher, Boston, 20 (1995) 237–297
Craven, M., Shavlik, J.: Using Neural Networks for Data Mining. Future Generation Computer Systems, 13 (1997) 211–229
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, Cambridge, (2000)
Domingos, P.: Unifying Instance-Based and rule-Based Induction. Machine Learning, Kluwer Academic Publisher, Boston, 24 (1991)141–168
Duda, R. O., Hart, P. R, Stork, D. G.: Pattern Recognition, John Wiley & Sons, Inc., New York, (2001)
Mcgarry K., Wermter, S., Maclntyre, J.: Hybrid Neural Systems: From Simple Coupling to Fully Integrad Neural Networks. Neural Computing Surveys, 2 (1999).
Merz, C. J., Murphy, P. M. Murphy.: UCI Repository for Machine Learning Data-Bases. Irvine, CA: University of California, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLRepository.html], (1998)
Mitra, S., Pal, S.K., Mitra, P.: Data Mining in Soft Computing Framework: A survey. IEEE Transactions on Neural Networks, 13(1) (2002) 3–14
Salzberg, S.: A Nearest Hyperrectangle Learning Method. Machine Learning, Kluwer Academic Publisher, Boston, 6 (1991) 251–276.
Tickle, A., Andrews, R., Mostefa, G., Diederich, J.: The Truth will come to light: Directions and Challenges in Extracting the Knowledge Embedded within Trained Artificial Neural Networks. IEEE Trans. on Neural Networks, 9(6) (1998)1057–1068
Schölkopf, B., Burges, C, Vapnik, V.: Incorporating Incariances in Support Vector Learning Machine. ICANN’96. Lecture Notes in Computer Science, 1112 (1996) 47–52
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, Inc., New York, (1998)
Wermter, S., Sun, R. (Eds): Hybrid Neural Systems. Springer-Verlag, Berlin, (2000)
Zhang, X.: Using Class-Center vectors to build Support Vector Machines. Proc. IEEE Conference on Neural Networks for Signal Processing, (1999) 3–11
Zhao, Q., Principe, J.C.: Improving ATR Performance by Incorporating Virtual negative examples. Proc. International Joint Conference on Neural Networks. 5 (1999) 3198–3203
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T., Müller, K.-R.: Engineering Support Vector Machine kernels that Recognize Translation Initiation Sites. Bioinformatics. 16(9) (2000) 799–807
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Núñez, H., Angulo, C., Catala, A. (2003). Hybrid Architecture Based on Support Vector Machines. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_82
Download citation
DOI: https://doi.org/10.1007/3-540-44868-3_82
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40210-7
Online ISBN: 978-3-540-44868-6
eBook Packages: Springer Book Archive