From post

GNN-DES: A New End-to-End Dynamic Ensemble Selection Method Based on Multi-label Graph Neural Network.

, , , и . GbRPR, том 14121 из Lecture Notes in Computer Science, стр. 59-69. Springer, (2023)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed.

 

Другие публикации лиц с тем же именем

Evolving Classifier Ensembles using Dynamic Multi-objective Swarm Intelligence., , и . ICPRAM, стр. 206-215. SciTePress, (2013)Annealing Based Approach to Optimize Classification Systems., , и . IJCNN, стр. 2616-2620. IEEE, (2007)Contribution of data complexity features on dynamic classifier selection., , , , и . IJCNN, стр. 4396-4403. IEEE, (2016)Combining Diversity and Classification Accuracy for Ensemble Selection in Random Subspaces., , и . IJCNN, стр. 2144-2151. IEEE, (2006)DESlib: A Dynamic ensemble selection library in Python., , , и . CoRR, (2018)Meta-regression based pool size prediction scheme for dynamic selection of classifiers., , , и . ICPR, стр. 216-221. IEEE, (2016)Analyzing features learned for Offline Signature Verification using Deep CNNs., , и . ICPR, стр. 2989-2994. IEEE, (2016)Fast two-level Viterbi search algorithm for unconstrained handwriting recognition., , и . ICASSP, стр. 3537-3540. IEEE, (2002)Offline handwritten signature verification - Literature review., , и . IPTA, стр. 1-8. IEEE, (2017)Dynamic Selection of Ensembles of Classifiers Using Contextual Information., , и . MCS, том 5997 из Lecture Notes in Computer Science, стр. 145-154. Springer, (2010)