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Boosting algorithms

Time since Introduction, Seed Mass, and Genome Size Predict Successful Invaders among the Cultivated Vascular Plants of Hawaii

John Paul Schmidt, John M. Drake

Topology identification in distribution system via machine learning algorithms

Peyman Razmi, Mahdi Ghaemi Asl, Giorgio Canarella, Afsaneh Sadat Emami

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

Ying Li, Nan Wang, [ ... ], Ping Gong

Predicting risk for trauma patients using static and dynamic information from the MIMIC III database

Evan J. Tsiklidis, Talid Sinno, Scott L. Diamond

Classification of masked image data

Kamila Lis, Mateusz Koryciński, Konrad A. Ciecierski

3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods

Ye Wei, Zirong Peng, [ ... ], Baptiste Gault

Ensemble learning method for the prediction of new bioactive molecules

Lateefat Temitope Afolabi, Faisal Saeed, Haslinda Hashim, Olutomilayo Olayemi Petinrin

Sample Selection for Training Cascade Detectors

Noelia Vállez, Oscar Deniz, Gloria Bueno

Chronic kidney disease prediction using boosting techniques based on clinical parameters

Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Saurav Mallik, Zhongming Zhao

Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables

Tianao Wu, Wei Zhang, [ ... ], Yousef Alhaj Hamoud

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach

Tanmay Nath, Rexford S. Ahima, Prasanna Santhanam

Classification of mathematical test questions using machine learning on datasets of learning management system questions

Gun Il Kim, Sungtae Kim, Beakcheol Jang

Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors

Montaser Abdelsattar, Mohamed A. Ismeil, [ ... ], Ahmed Emad-Eldeen

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