Apple tasting and nearly one-sided learning

Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS

Volumn 1992-October, Issue , 1992, Pages 493-502

  Helmbold, David P ^a   Littlestone, Nicholas ^b   Long, Philip M ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NEC LABORATORIES AMERICA   (United States)

^c GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS;

BOOLEAN VARIABLES; FALSE ACCEPTANCE; FALSE REJECTION; ONLINE MODELING; STANDARD MODEL; THE STANDARD MODEL;

FRUITS;

EID: 2342562085     PISSN: 02725428     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SFCS.1992.267802     Document Type: Conference Paper

References (18)

1. D. Angluin. Queries and concept learning. Machine Learning, 2:319-342, 1988.
2. J. Berger. Statistical Decision Theory and Bayesian Analysis. Springer Verlag, 1980.
3. A. Blum. Learning boolean functions in an infinite attribute space. Proceedings of the 22nd ACM Symposium on the Theory of Computation, 1990.
4. A. Blum. Separating PAC and mistake-bound learning models over the boolean domain. Proceedings of the 31st Annual Symposium on the Foundations of Computer Science, 1990.
5. A. Blum, L. Hellerstein, and N. Littlestone. Learning in the presence of finitely many or infinitely many irrelevant attributes. The 1991 Workshop on Computational Learning Theory, 1991.
6. W. Feller. An Introduction to Probability and its Applications, volume 1. John Wiley and Sons, third edition, 1968.
7. D. Haussler, N. Littlestone, and M. Warmuth. Predicting {0, 1} functions on randomly drawn points. Technical report, University of California at Santa Cruz, 1990.
8. D. Helmbold, N. Littlestone, and P. M. Long. On-line learning with linear loss constraints. In preparation, 1992.
9. D. Helmbold, R. Sloan, and M. Warmuth. Learning nested differences of intersection-closed concept classes. Machine Learning, 5:165-196, 1990.
10. N. Littlestone. Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Machine Learning, 2:285-318, 1988.
11. N. Littlestone. Mistake Bounds and Logarithmic Linear-threshold Learning Algorithms. PhD thesis, UC Santa Cruz, 1989.
12. N. Littlestone and M. Warmuth. The weighted majority algorithm. Proceedings of the 30th Annual Symposium on the Foundations of Computer Science, 1989.
13. W. Maass. On-line learning with an oblivious environment and the power of randomization. The 1991 Workshop on Computational Learning Theory, pages 167-175, 1991.
14. W. Maass and G. Turan. On the complexity of learning from counterexamples. Proceedings of the 30th Annual Symposium on the Foundations of Computer Science, 1989.
15. W. Maass and G. Turan. On the complexity of learning from counterexamples and membership queries. Proceedings of the 31st Annual Symposium on the Foundations of Computer Science, 1990.
16. B. Natarajan. Probably approximate learning of sets and functions. Siam J. Comput., 10(2):328-351, 1991.
17. L. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.
18. V. Vapnik and A. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16(2):264-280, 1971.