Efficiency versus convergence of boolean kernels for on-line learning algorithms

Journal of Artificial Intelligence Research

Volumn 24, Issue , 2005, Pages 341-356

  Khardon, Roni ^a   Roth, Dan ^b   Servedio, Rocco A ^c  

^a Tufts University   (United States)

^b University of Illinois at Urbana Champaign   (United States)

^c Department of Earth and Environmental Sciences   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; LEARNING ALGORITHMS; NEURAL NETWORKS; ONLINE SYSTEMS; POLYNOMIALS; PROBLEM SOLVING; THRESHOLD ELEMENTS;

BOOLEAN FEATURES; CONJUNCTIONS; KERNELS; ON-LINE LEARNING;

LEARNING SYSTEMS;

EID: 31144459461     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1655     Document Type: Article

References (22)

1. Angluin, D. (1990). Negative results for equivalence queries. Machine Learning, 2, 121-150.
2. Block, H. (1962). The perception: a model for brain functioning. Reviews of Modern Physics, 34, 123-135.
3. Carlson, A., Cumby, C., Rosen, J., & Roth, D. (1999). The SNoW learning architecture. Tech. rep. UIUCDCS-R-99-2101, UIUC Computer Science Department.
4. Chawla, D., Li, L., & Scott., S. (2004). On approximating weighted sums with exponentially many terms. Journal of Computer and System Sciences, 69, 196-234.
5. Cristianini, N., & Shaw-Taylor, J. (2000). An Introduction to Support Vector Machines. Cambridge Press.
6. Cumby, C., & Roth, D. (2003). On kernel methods for relational learning. In Proc. of the International Conference on Machine Learning.
7. Kearns, M., & Vazirani, U. (1994). An Introduction to Computational Learning Theory. MIT Press, Cambridge, MA.
8. Khardon, R., Roth, D., & Servedio, R. (2002). Efficiency versus convergence of Boolean kernels for on-line learning algorithms. In Dietterich, T. G., Becker, S., & Ghahramani, Z. (Eds.), Advances in Neural Information Processing Systems 14, Cambridge, MA. MIT Press.
9. Khardon, R., & Servedio, R. (2003). Maximum margin algorithms with Boolean kernels. In Proceedings of the Sixteenth Annual Conference on Computational Learning Theory, pp. 87-101.
10. Lint, J. V. (1992). Introduction to Coding Theory. Springer-Verlag.
11. Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2, 285-318.
12. Maass, W., & Warmuth, M. K. (1998). Efficient learning with virtual threshold gates. Information and Computation, 141(1), 378-386.
13. Minsky, M., & Papert, S. (1968). Perceptrons: an introduction to computational geometry. MIT Press, Cambridge, MA.
14. Novikoff, A. (1963). On convergence proofs for perceptions. In Proceeding of the Symposium on the Mathematical Theory of Automata, Vol. 12, pp. 615-622.
15. Papadimitriou, C. (1994). Computational Complexity. Addison-Wesley.
16. Rosenblatt, F. (1958). The Perception: a probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386-407.
17. Roth, D. (1998). Learning to resolve natural language ambiguities: A unified approach. In Proc. of the American Association of Artificial Intelligence, pp. 806-813.
18. Sadohara, K. (2001). Learning of Boolean functions using support vector machines. In Proc. of the Conference on Algorithmic Learning Theory, pp. 106-118. Springer. LNAI 2225.
19. Takimoto, E., & Warmuth, M. (2003). Path kernels and multiplicative updates. Journal of Machine Learning Research, 4, 773-818.
20. Valiant, L. G. (1979). The complexity of enumeration and reliability problems. SIAM Journal of Computing, 8, 410-421.
21. Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134-1142.
22. Watkins, C. (1999). Kernels from matching operations. Tech. rep. CSD-TR-98-07, Computer Science Department, Royal Holloway, University of London.