| Hyperspectral remote sensing scenes M Grana, MA Veganzons, B Ayerdi Grupo de Inteligencia Computacional (GIC), 2020 | 144 | 2020 |
| Random forest active learning for AAA thrombus segmentation in computed tomography angiography images J Maiora, B Ayerdi, M Graña Neurocomputing 126, 71-77, 2014 | 63 | 2014 |
| Spatially regularized semisupervised ensembles of extreme learning machines for hyperspectral image segmentation B Ayerdi, I Marqués, M Graña Neurocomputing 149, 373-386, 2015 | 43 | 2015 |
| Discrimination between Alzheimer’s disease and late onset bipolar disorder using multivariate analysis A Besga, I Gonzalez, E Echeburua, A Savio, B Ayerdi, D Chyzhyk, ... Frontiers in Aging Neuroscience 7, 231, 2015 | 40 | 2015 |
| Hybrid extreme rotation forest B Ayerdi, M Graña Neural Networks 52, 33-42, 2014 | 34 | 2014 |
| Hyperspectral image analysis by spectral–spatial processing and anticipative hybrid extreme rotation forest classification B Ayerdi, MG Romay IEEE Transactions on Geoscience and Remote Sensing 54 (5), 2627-2639, 2015 | 29 | 2015 |
| Hyperspectral image nonlinear unmixing and reconstruction by ELM regression ensemble B Ayerdi, M Graña Neurocomputing 174, 299-309, 2016 | 27 | 2016 |
| Risk factors for emergency department short time readmission in stratified population A Besga, B Ayerdi, G Alcalde, A Manzano, P Lopetegui, M Graña, ... BioMed research international 2015 (1), 685067, 2015 | 25 | 2015 |
| Active learning with bootstrapped dendritic classifier applied to medical image segmentation D Chyzhyk, B Ayerdi, J Maiora Pattern Recognition Letters 34 (14), 1602-1608, 2013 | 18 | 2013 |
| Meta-ensembles of classifiers for Alzheimer’s disease detection using independent ROI features B Ayerdi, A Savio, M Graña International Work-Conference on the Interplay Between Natural and …, 2013 | 18 | 2013 |
| Using anticipative hybrid extreme rotation forest to predict emergency service readmission risk A Artetxe, B Ayerdi, M Graña, S Rios Journal of Computational Science 20, 154-161, 2017 | 17 | 2017 |
| Hyperspectral Remote sensing scenes-grupo de Inteligencia computacional (GIC) M Grana, MA Veganzons, B Ayerdi URL https://www. ehu. eus/ccwintco/index. php/Hyperspectral_Remote …, 2021 | 12 | 2021 |
| Hyperspectral Remote Sensing Scenes. 2021 M Graña, MA Veganzons, B Ayerdi URL https://www. ehu. eus/ccwintco/index. php/Hyperspectral_Remote …, 0 | 11 | |
| Eigenanatomy on fractional anisotropy imaging provides white matter anatomical features discriminating between Alzheimer’s disease and late onset bipolar disorder A Besga, D Chyzhyk, I González-Ortega, A Savio, B Ayerdi, J Echeveste, ... Current Alzheimer Research 13 (5), 557-565, 2016 | 10 | 2016 |
| Active learning of hybrid extreme rotation forests for CTA image segmentation B Ayerdi, J Maiora, M Graña 2012 12th International Conference on Hybrid Intelligent Systems (HIS), 543-548, 2012 | 8 | 2012 |
| Random forest active learning for retinal image segmentation B Ayerdi, M Graña Proceedings of the 9th International Conference on Computer Recognition …, 2016 | 7 | 2016 |
| Applications of hybrid extreme rotation forests for image segmentation B Ayerdi, J Maiora, A d'Anjou, M Graña International Journal of Hybrid Intelligent Systems 11 (1), 13-24, 2014 | 7 | 2014 |
| Anticipative hybrid extreme rotation forest B Ayerdi, M Graña Procedia Computer Science 80, 1671-1681, 2016 | 5 | 2016 |
| Random forest active learning for computed tomography angiography image segmentation J Maiora, B Ayerdi, M Graña Neurocomputing, 2013 | 5 | 2013 |
| A new heuristic for influence maximization in social networks JD Nuñez-Gonzalez, B Ayerdi, M Graña, M Wozniak Logic Journal of the IGPL 24 (6), 996-1014, 2016 | 3 | 2016 |
Manuel grañaUpv/ehuDirección de correo verificada de si.ehu.es
