The perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant
- 31 December 1997
- journal article
- research article
- Published by Elsevier in Artificial Intelligence
- Vol. 97 (1-2) , 325-343
- https://doi.org/10.1016/s0004-3702(97)00039-8
Abstract
No abstract availableKeywords
This publication has 8 references indexed in Scilit:
- Exponentiated Gradient versus Gradient Descent for Linear PredictorsInformation and Computation, 1997
- On-line learning of linear functionscomputational complexity, 1995
- The statistical mechanics of learning a ruleReviews of Modern Physics, 1993
- Learnability and the Vapnik-Chervonenkis dimensionJournal of the ACM, 1989
- Learning quickly when irrelevant attributes abound: A new linear-threshold algorithmMachine Learning, 1988
- A theory of the learnableCommunications of the ACM, 1984
- On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesTheory of Probability and Its Applications, 1971
- The perceptron: A probabilistic model for information storage and organization in the brain.Psychological Review, 1958