DocumentCode
1025365
Title
Improved Risk Tail Bounds for On-Line Algorithms
Author
Cesa-Bianchi, Nicolò ; Gentile, Claudio
Author_Institution
Milano Univ., Milano
Volume
54
Issue
1
fYear
2008
Firstpage
386
Lastpage
390
Abstract
Tight bounds are derived on the risk of models in the ensemble generated by incremental training of an arbitrary learning algorithm. The result is based on proof techniques that are remarkably different from the standard risk analysis based on uniform convergence arguments, and improves on previous bounds published by the same authors.
Keywords
learning (artificial intelligence); risk analysis; statistical analysis; arbitrary learning algorithm; ensemble; incremental training; online algorithms; proof techniques; risk analysis; risk tail bounds; statistical learning theory; uniform convergence arguments; Algorithm design and analysis; Convergence; Inference algorithms; Information processing; Loss measurement; Performance loss; Risk analysis; Standards publication; Statistical learning; Tail; Martingales; on-line learning; risk bounds; statistical learning theory;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.2007.911292
Filename
4418464
Link To Document