116 search results - page 7 / 24 » Empirical Bernstein Boosting |
COLT
2005
Springer
14 years 3 months ago
2005
Springer
Abstract. We present several results related to ranking. We give a general margin-based bound for ranking based on the L∞ covering number of the hypothesis space. Our bound sugge...
KDD
1999
ACM
14 years 2 months ago
1999
ACM
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
IR
2010
13 years 8 months ago
2010
Abstract We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empiricall...
COLT
2005
Springer
14 years 3 months ago
2005
Springer
A general model is proposed for studying ranking problems. We investigate learning methods based on empirical minimization of the natural estimates of the ranking risk. The empiric...
CORR
2007
Springer
13 years 9 months ago
2007
Springer
The utility of limited feedback for coding over an individual sequence of DMCs is investigated. This study complements recent results showing how limited or noisy feedback can boo...