Learning of boolean functions using support vector machines

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

Volumn 2225, Issue , 2001, Pages 106-118

  Sadohara, Ken ^a  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

SUPPORT VECTOR MACHINES;

DISCRETE ATTRIBUTES; DISJUNCTIVE NORMAL FORM; EMPIRICAL STUDIES; GAUSSIAN KERNELS; HIGH DIMENSIONAL SPACES; HIGH-DIMENSIONAL FEATURE SPACE; INPUT/OUTPUT BEHAVIORS; LINEAR FUNCTIONS;

BOOLEAN FUNCTIONS;

EID: 84948143092     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45583-3_10     Document Type: Conference Paper

References (16)

1. D. Angluin. Queries and concept learning. Machine Learning, 2:319-342, 1988.
2. M. Bellare and P. Rogaway. The complexity of approximating a nonlinear program. In Complexity of Numerical Optimization, pages 16-32. World Scientific, 1993.
3. V. Cherkassky and F. Mulier. Learning from Data. Wiley, 1998.
4. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge Press, 2000.
5. P. Domingos. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103-130, 1997.
6. P. Domingos. The role of Occam’s razor in knowledge discovery. Data Mining and Knowledge Discovery, 3:409-425, 1999.
7. M. Kearns, Ming Li, L. Pitt, and L.G. Valiant. On the learnability of Boolean formulae. ACM, pages 285-295, 1987.
8. R. Khardon, D. Roth, and R. Servedio. Efficiency versus convergence of Boolean kernels for on-line learning algorithms. Manuscript, 2001.
9. H. Liu and H. Motoda. Feature transformation and subset selection. IEEE Intelligent Systems, 13(2):26-28, 1998.
10. L. Pitt and L.G. Valiant. Computational limitations on learning from examples. Journal of the Association for Computing Machinery, 35(4):965-984, 1988.
11. J.C. Platt. Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods-Support Vector Learning, pages 185-208. MIT Press, 1998.
12. J.R. Quinlan. An empirical comparison of genetic and decision-tree classifiers. In Proceedings of International Conference on Machine Learning, pages 135-141, 1988.
13. J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
14. L.G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.
15. V. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, 1995.
16. V. Vapnik. Statistical Learning Theory. Wiley, 1998.