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NIPS
2001
13 years 7 months ago
2001
In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of hypotheses generated by an arbitrary on-line learning algorithm run on an independent an...
NIPS
1998
13 years 7 months ago
1998
ICML
2003
IEEE
14 years 6 months ago
2003
IEEE
The majority of theoretical work in machine learning is done under the assumption of exchangeability: essentially, it is assumed that the examples are generated from the same prob...
COLT
2007
Springer
13 years 12 months ago
2007
Springer
We consider on-line density estimation with the multivariate Gaussian distribution. In each of a sequence of trials, the learner must posit a mean µ and covariance Σ; the learner...
TIT
2008
13 years 5 months ago
2008
We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incrementally on the training data. Our result i...