„Boosting algorithm“

Suchergebnisse

221 Treffer

• Prediction of hot spots in protein–DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting
  Li, Ke ; Zhang, Sijia ; Yan, Di ; Bin, Yannan ; Xia, Junfeng
• LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting
  Zhang, Yuan ; Ye, Fei ; Xiong, Dapeng ; Gao, Xieping
• A comparative analysis of gradient boosting algorithms
  Bentéjac, Candice ; Csörgő, Anna ; Martínez-Muñoz, Gonzalo
• PFBNet: a priori-fused boosting method for gene regulatory network inference
  Che, Dandan ; Guo, Shun ; Jiang, Qingshan ; Chen, Lifei
• A Passive Islanding Detection Algorithm Based on Modal Current and Adaptive Boosting
  Bhatt, Nimish ; Kumar, Ashwani
• PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences
  Yan, Cheng ; Wu, Fang-Xiang ; Wang, Jianxin ; Duan, Guihua
• Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network
  Liu, Hui ; Zhang, Wenhao ; Nie, Lixia ; Ding, Xiancheng ; Luo, Judong ; Zou, Ling
• Boosting sensitivity of a retinal vessel segmentation algorithm
  Khan, Mohammad A. U. ; Khan, Tariq M. ; Soomro, Toufique Ahmed ; Mir, Nighat ; Gao, Junbin
• BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets
  Zagganas, Konstantinos ; Vergoulis, Thanasis ; Paraskevopoulou, Maria D. ; Vlachos, Ioannis S. ; Skiadopoulos, Spiros ; Dalamagas, Theodore
• A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection
  Saien, Soudeh ; Moghaddam, Hamid Abrishami ; Fathian, Mohsen
• A multiclass boosting algorithm to labeled and unlabeled data
  Tanha, Jafar
• Extending Statistical Boosting – An Overview of Recent Methodological Developments
  Mayr, A. ; Binder, H. ; Gefeller, O. ; Schmid, M.
• Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”
  Bühlmann, P. ; Gertheiss, J. ; Hieke, S. ; Kneib, T. ; Ma, S. ; Schumacher, M. ; Tutz, G. ; Wang, C. -Y. ; Wang, Z. ; Ziegler, A.
• The Evolution of Boosting Algorithms – From Machine Learning to Statistical Modelling
  Mayr, A. ; Binder, H. ; Gefeller, O. ; Schmid, M.
• PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine
  Deng, Lei ; Pan, Juan ; Xu, Xiaojie ; Yang, Wenyi ; Liu, Chuyao ; Liu, Hui
• INBIA: a boosting methodology for proteomic network inference
  Sardina, Davide S. ; Micale, Giovanni ; Ferro, Alfredo ; Pulvirenti, Alfredo ; Giugno, Rosalba
• A boosting approach for prediction of protein-RNA binding residues
  Tang, Yongjun ; Liu, Diwei ; Wang, Zixiang ; Wen, Ting ; Deng, Lei
• BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data
  Guo, Yang ; Liu, Shuhui ; Li, Zhanhuai ; Shang, Xuequn
• Diabetes classification model based on boosting algorithms
  Chen, Peihua ; Pan, Chuandi
• Personalized mobile physical activity monitoring for everyday life
  Reiss, Attila