| W02-2024 | languages : Spanish and Dutch . A | boosted decision tree | method obtained the best performance |
| D09-1025 | extraction system . A gradient | boosted decision tree | is used to learn a regression |
| P13-1171 | logistic regression ( LR ) and | boosted decision trees | ( BDT ) . As mentioned in Sec |
| P13-1171 | ting : logistic regression and | boosted decision trees | ( Friedman , 2001 ) . The former |
| W01-0708 | three parts of the shared task the | boosted decision tree | system of Carreras and Marquez |
| P08-1109 | define a simple linear model , use | boosted decision trees | to select feature conjunctions |
| W04-3211 | Forest approach outperforms the | Boosted Decision Tree | method by 3.5 % , but trails |
| W02-2004 | binary propositional features . The | boosted decision trees | construct conjunctions of such |
| D15-1054 | al. ( 2012 ) used a variant of | boosted decision trees | with similar features . Richardson |
| D10-1110 | − f ( x ) We employ Gradient | Boosted Decision Tree | algorithm ( Friedman , 2001 ) |
| P04-1045 | and Forbes , 2003 ) , the use of | boosted decision trees | yielded the most robust performance |
| W04-3211 | random forest classifier to the | boosted decision tree | and the SVM using all of the |
| D10-1110 | the formula below , . Gradient | Boosted Decision Tree | is an additive regression algorithm |
| E12-1023 | vector regression and gradient | boosted decision trees | to select the most relevant sentences |
| D09-1055 | sequentially selected to build the | boosted decision trees | . The split of each node increases |
| D09-1025 | Specifically , we use a Gradient | Boosted Decision Tree | regression model - GBDT ( Fried |
| W04-3211 | feature set and outperforms the | boosted decision tree | classifier ( Surdeanu et al. |
| P13-1141 | experiments on development data using | boosted decision trees | instead and other loss functions |
| Q13-1032 | logistic regression ( maxent ) and | boosted decision trees | , as well as the LSA metric for |
| W04-2326 | instantiated with the learning method ( | boosted decision trees | ) and feature set ( acoustic |
