Search Results for author: Shaodan Zhai

Found 8 papers, 0 papers with code

A simple discriminative training method for machine translation with large-scale features

no code implementations15 Sep 2019 Tian Xia, Shaodan Zhai, Shaojun Wang

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.

Machine Translation Translation

Plackett-Luce model for learning-to-rank task

no code implementations15 Sep 2019 Tian Xia, Shaodan Zhai, Shaojun Wang

List-wise based learning to rank methods are generally supposed to have better performance than point- and pair-wise based.

Learning-To-Rank

Analysis of Regression Tree Fitting Algorithms in Learning to Rank

no code implementations12 Sep 2019 Tian Xia, Shaodan Zhai, Shaojun Wang

In learning to rank area, industry-level applications have been dominated by gradient boosting framework, which fits a tree using least square error principle.

Learning-To-Rank

Une m\'ethode discriminant formation simple pour la traduction automatique avec Grands Caract\'eristiques

no code implementations JEPTALNRECITAL 2015 Tian Xia, Shaodan Zhai, Zhongliang Li, Shaojun Wang

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.

Direct 0-1 Loss Minimization and Margin Maximization with Boosting

no code implementations NeurIPS 2013 Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang

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.

Classification General Classification

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