Identification Criteria and Lower Bounds for Perceptron-Like Learning Rules

Neural Computation

Volumn 10, Issue 1, 1998, Pages 235-250

  Schmitt, Michael ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001504665     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300017962     Document Type: Article

References (27)

1. Angluin, D. (1988). Queries and concept learning. Machine Learning, 2, 319-342.
2. Anthony, M., Brightwell, G., & Shawe-Taylor, J. (1995). On specifying boolean functions by labelled examples. Discrete Applied Mathematics, 61, 1-25.
3. Anthony, M., & Shawe-Taylor, J. (1993). Using the Perceptron algorithm to find consistent hypotheses. Combinatorics, Probability and Computing, 2, 385-387.
4. Bartlett, P. L., & Williamson, R. C. (1991). Investigating the distribution assumptions in the PAC learning model. In L. G. Valiant, & M. K. Warmuth (Eds.), Proceedings of the Fourth Annual Workshop on Computational Learning Theory (pp. 24-32). San Mateo, CA: Morgan Kaufmann.
5. Blum, A., Frieze, A., Kannan, R., & Vempala, S. (in press). A polynomial-time algorithm for learning noisy linear threshold functions. In Proceedings of the 37th Annual Symposium on Foundations of Computer Science.
6. Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989). Learnability and the Vapnik-Chervonenkis dimension. Journal of the Association for Computing Machinery, 36, 929-965.
7. Hampson, S. E., & Volper, D. J. (1986). Linear function neurons: Structure and training. Biological Cybernetics, 53, 203-217.
8. Håstad, J. (1994). On the size of weights for threshold gates. SIAM Journal on Discrete Mathematics, 7, 484-492.
9. Kivinen, J., & Warmuth, M. K. (1995). The Perceptron algorithm vs. Winnow: Linear vs. logarithmic mistake bounds when few input variables are relevant. In Proceedings of the Eighth Annual Conference on Computational Learning Theory (pp. 289-296). New York: ACM Press.
10. Lewis II, P. M. (1966). A lower bound on the number of corrections required for convergence of the single threshold gate adaptive procedure. IEEE Transactions on Electronic Computers, 15, 933-935.
11. Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2, 285-318.
12. Maass, W. (1994). Perspectives of current research about the complexity of learning on neural nets. In V. Roychowdhury, K.-Y. Siu, & A. Orlitsky (Eds.), Theoretical advances in neural computation and learning (pp. 295-336). Boston: Kluwer.
13. Maass, W., & Turán, G. (1994). How fast can a threshold gate learn? In S. J. Hanson, G. Drastal, & R. Rivest (Eds.), Computational learning theory and natural learning systems: Constraints and prospects (pp. 381-414). Cambridge, MA: MIT Press.
14. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133.
15. McNaughton, R. (1961). Unate truth functions. IRE Transactions on Electronic Computers, 10, 1-6.
16. Minsky, M. L., & Papert, S. A. (1988). Perceptrons: An introduction to computational geometry (Exp. ed.). Cambridge, MA: MIT Press.
17. Muroga, S. (1971). Threshold logic and its applications. New York: Wiley.
18. Nagy, G. (1991). Neural networks - then and now. IEEE Transactions on Neural Networks, 2, 316-318.
19. Novikoff, A. (1962). On convergence proofs for Perceptrons. In Symposium on Mathematical Theory of Automata (pp. 615-622). Brooklyn, NY: Polytechnic Institute of Brooklyn.
20. Palm, G. (1991). Memory capacities of local rules for synaptic modification: A comparative review. Concepts in Neuroscience, 2, 97-128.
21. Parberry, I. (1994). Circuit complexity and neural networks. Cambridge, MA: MIT Press.
22. Paull, M. C., & McCluskey, Jr., E. J. (1960). Boolean functions realizable with single threshold devices. Proceedings of the IRE, 48, 1335-1337.
23. Pitt, L., & Valiant, L. G. (1988). Computational limitations on learning from examples. Journal of the Association for Computing Machinery, 35, 965-984.
24. Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386-408.
25. Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Washington, DC: Spartan Books.
26. Schmitt, M., (1994). On the size of weights for McCulloch-Pitts neurons. In E. R. Caianiello (Ed.), Proceedings of the Sixth Italian Workshop on Neural Nets WIRN VIETRI-93 (pp. 241-246). Singapore: World Scientific.
27. Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27, 1134-1142.