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Found 8 papers, 0 papers with code
Direct 0-1 Loss Minimization and Margin Maximization with Boosting
We propose a boosting method, DirectBoost, a greedy coordinate descent algorithm that builds an ensemble classifier of weak classifiers through directly minimizing empirical classification error over labeled training examples; once the training classification error is reduced to a local coordinatewise minimum, DirectBoost runs a greedy coordinate ascent algorithm that continuously adds weak classifiers to maximize any targeted arbitrarily defined margins until reaching a local coordinatewise maximum of the margins in a certain sense.
Analysis of Regression Tree Fitting Algorithms in Learning to Rank
In learning to rank area, industry-level applications have been dominated by gradient boosting framework, which fits a tree using least square error principle.
Plackett-Luce model for learning-to-rank task
List-wise based learning to rank methods are generally supposed to have better performance than point- and pair-wise based.
A simple discriminative training method for machine translation with large-scale features
Margin infused relaxed algorithms (MIRAs) dominate model tuning in statistical machine translation in the case of large scale features, but also they are famous for the complexity in implementation.
Une m\'ethode discriminant formation simple pour la traduction automatique avec Grands Caract\'eristiques
Marge infus{\'e} algorithmes d{\'e}tendus (MIRAS) dominent mod{\`e}le de tuning dans la traduction automatique statistique dans le cas des grandes caract{\'e}ristiques de l{'}{\'e}chelle, mais ils sont {\'e}galement c{\'e}l{\`e}bres pour la complexit{\'e} de mise en {\oe}uvre.
Learning to Rank For Push Notifications Using Pairwise Expected Regret
Listwise ranking losses have been widely studied in recommender systems.
Should I send this notification? Optimizing push notifications decision making by modeling the future
In this work we focus on mobile push notifications, where the long term effects of recommender system decisions can be particularly strong.
