Approximate maximum margin algorithms with rules controlled by the number of mistakes

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 903-910

  Tsampouka, Petroula ^a   Shawe Taylor, John ^b  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CONVERGENCE OF NUMERICAL METHODS; SUPPORT VECTOR MACHINES;

MAXIMUM MARGIN ALGORITHMS; MISTAKES; WEIGHT VECTOR;

NEURAL NETWORKS;

EID: 34547971583     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273610     Document Type: Conference Paper

References (12)

1. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
2. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge University Press.
3. Duda, R. O., & Hart, P. E. (1973). Pattern classsification and scene analysis. Wiley.
4. Gentile, C. (2001). A new approximate maximal margin classification algorithm. Machine Learning Research, 2, 213-242.
5. Joachims, T. (1999). Making large-scale svm learning practical. In Advances in kernel methods-support vector learning. MIT Press.
6. Joachims, T. (2006). Training linear svms in linear time. KDD'06 (pp. 217-226). ACM Press.
7. Li, Y., & Long, Y. (2002). The relaxed online maximum margin algorithm. Machine Learning, 46, 361-387.
8. Platt, J. C. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines (Technical Report MSR-TR-98-14). Microsoft Research.
9. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65 (6), 386-408.
10. Tsampouka, P., & Shawe-Taylor, J. (2005). Analysis of generic perceptron-like large margin classifiers. ECML 2005 (pp. 750-758). Springer-Verlag.
11. Tsampouka, P., & Shawe-Taylor, J. (2006). Constant rate approximate maximum margin algorithms. ECML 2006 (pp. 437-448). Springer-Verlag.
12. Vapnik, V. (1998). Statistical learning theory. Wiley.