G Abor Lugosi, Nicolas Vayatis
sparsity should play a key role. We believe that the analysis of consistency provides valuable insight into the behavior of boosting. Indeed, building partly on the techniques of the discussed papers...
The Annals of Statistics (2003)
this paper, a "cleaner" related estimate is proposed, and explicit nonasymptotic performance guarantees are provided that are uniform over all f. Received June 1996; revised June 1997
Concentration Inequalities Using the Entropy Method (2002)
We investigate a new methodology... The main purpose of this paper is to point out the simplicity and the generality of the approach. We show how the new method can recover many of Talagrand's...
Concentration Inequalities Using the Entropy Method (2001)
this paper is to point out the simplicity and the generality of the approach. We show how the new method can recover many of Talagrand's revolutionary inequalities and provide new applications in a...
Model Selection and Error Estimation (2000)
Peter L. Bartlett, St Ephane Boucheron, G Abor Lugosi
We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error...
Minimax Regret under Log Loss for General Classes of Experts (1999)
Nicol O Cesa-bianchi, Polo Didattico E Di Ricerca, G Abor Lugosi
We study sequential strategies for assigning probabilities to the elements that may appear next in a sequence of data. The goal is to minimize the regret under log loss over the worst possible...
Minimax Regret under Log Loss for General Classes of Experts (1999)
Nicol O Cesa-bianchi, Polo Didattico E Di Ricerca, G Abor Lugosi
We study sequential strategies for assigning probabilities to the elements that may appear next in a sequence of data. The goal is to minimize the regret under log loss over the worst possible...