Margin distribution bounds on generalization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1572, Issue , 1999, Pages 263-273

  Shawe Taylor, John ^a   Cristianini, Nello ^a  

^a UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; MACHINE LEARNING;

ERROR BOUND; GENERALIZATION ERROR; KERNEL-BASED LEARNING; MARGIN DISTRIBUTIONS; SAMPLE SIZES; TRAINING POINTS; TRAINING SETS;

LEARNING ALGORITHMS;

EID: 33846693694     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-49097-3_21     Document Type: Conference Paper

References (15)

1. Peter Bartlett, Pattern Classification in Neural Networks, IEEE Transactions on Information Theory, to appear.
2. Peter Bartlett and John Shawe-Taylor, Generalization Performance of Support Vector Machines and Other Pattern Classifiers, In 'Advances in Kernel Methods-Support Vector Learning', Bernhard Scholkopf, Christopher J. C. Burges, and Alexander J. Smola (eds.), MIT Press, Cambridge, USA, 1998.
3. C. Cortes and V. Vapnik, Support-Vector Networks, Machine Learning, 20(3): 273-297, September 1995.
4. Nello Cristianini, John Shawe-Taylor, and Peter Sykacek, Bayesian Classifiers are Large Margin Hyperplanes in a Hilbert Space, in Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers, San Francisco, CA.
5. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, New York: Wiley, 1973.
6. Yoav Freund and Robert E. Schapire, Large Margin Classification Using the Per-ceptron Algorithm, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 1998.
7. Leonid Gurvits, A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces. In Proceedings of Algorithm Learning Theory, ALT-97, and as NECI Technical Report, 1997.
8. Norbert Klasner and Hans Ulrich Simon, From Noise-Free to Noise-Tolerant and from On-line to Batch Learning, Proceedings of the Eighth Annual Conference on Computational Learning Theory, COLT'95, 1995, pp. 250-257.
9. R. Schapire, Y. Freund, P. Bartlett, W. Sun Lee, Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. In D. H. Fisher, Jr., editor, Proceedings of International Conference on Machine Learning, ICML'97, pages 322330, Nashville, Tennessee, July 1997. Morgan Kaufmann Publishers.
10. John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony, Structural Risk Minimization over Data-Dependent Hierarchies, to appear in IEEE Trans. on Inf. Theory, and NeuroCOLT Technical Report NC-TR-96-053, 1996. (ftp://ftp. des. rhbnc. ac. uk/pub/neurocolt/tech_reports).
11. John Shawe-Taylor and Robert C. Williamson, Generalization Performance of Classifiers in Terms of Observed Covering Numbers, Submitted to EuroCOLT'99, 1998.
12. University of California, Irvine-Machine Learning Repository, http://www. ics. uci. edu/mlearn/MLRepository. html.
13. Vladimir N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
14. Vladimir N. Vapnik, Estimation of Dependences Based on Empirical Data, Springer-Verlag, New York, 1982.
15. Vladimir N. Vapnik, Esther Levin and Yann Le Cunn, Measuring the VC-dimension of a learning machine, Neural Computation, 6: 851-876, 1994.