Abstract
SVMs (support vector machines) have met with a significant success in information retrieval field, especially handling text classification tasks. Although various performance estimators for SVMs have been proposed, they only focus on the accuracy based on the LOO (leave-one-out) cross validation procedure. The information-retrieval-related performance measures are always neglected in kernel learning methodology. In this paper, we have proposed a set of information-retrieval-oriented performance estimators for SVMs, which are based on the span bound of the LOO procedure. Experiments have proved that our proposed estimators are both effective and stable. ...
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
Jochims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Jochims, T.: Transductive inference for text classification using support vector machines. In: Proceedings of ICML, 16th International Conference on Machine Learning, Bled, Slovenia, pp. 200–209 (1999)
Joachims, T.: Estimating the generalization performance of a SVM efficiently. Proceedings of the International Conference on Machine Learning, San Mateo, CA, pp. 431–438 (2000)
Wahba, G., Lin, Y., Zhang, H.: GACV for support vector machines. In: Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 297–311. MIT Press, Cambridge (1999)
Vapnik, V., Chapelle, O.: Bounds on error expectation for support vector machine. In: Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 261–280. MIT Press, Cambridge (1999)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46, 131–159 (2002)
Lunts, A., Brailovskiy, V.: Evaluation of attributes obtained in statistical decision rules. Engineering cybernetics 3, 98–109 (1967)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Learning to Classify Text from Labeled and Unlabeled Documents. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI 1998), Madison, Wisconsin, pp. 792–799 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, S., Song, H., Ma, F. (2004). Novel SVM Performance Estimators for Information Retrieval Systems. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_100
Download citation
DOI: https://doi.org/10.1007/978-3-540-24655-8_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21371-0
Online ISBN: 978-3-540-24655-8
eBook Packages: Springer Book Archive