The Annals of Statistics
- Ann. Statist.
- Volume 32, Number 4 (2004), 1698-1722.
Generalization bounds for averaged classifiers
We study a simple learning algorithm for binary classification. Instead of predicting with the best hypothesis in the hypothesis class, that is, the hypothesis that minimizes the training error, our algorithm predicts with a weighted average of all hypotheses, weighted exponentially with respect to their training error. We show that the prediction of this algorithm is much more stable than the prediction of an algorithm that predicts with the best hypothesis. By allowing the algorithm to abstain from predicting on some examples, we show that the predictions it makes when it does not abstain are very reliable. Finally, we show that the probability that the algorithm abstains is comparable to the generalization error of the best hypothesis in the class.
Ann. Statist., Volume 32, Number 4 (2004), 1698-1722.
First available in Project Euclid: 4 August 2004
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62C12: Empirical decision procedures; empirical Bayes procedures
Freund, Yoav; Mansour, Yishay; Schapire, Robert E. Generalization bounds for averaged classifiers. Ann. Statist. 32 (2004), no. 4, 1698--1722. doi:10.1214/009053604000000058. https://projecteuclid.org/euclid.aos/1091626184