| Do we need hundreds of classifiers to solve real world classification problems? M Fernández-Delgado, E Cernadas, S Barro, D Amorim The journal of machine learning research 15 (1), 3133-3181, 2014 | 4258 | 2014 |
| An extensive experimental survey of regression methods M Fernández-Delgado, MS Sirsat, E Cernadas, S Alawadi, S Barro, ... Neural Networks 111, 11-34, 2019 | 311 | 2019 |
| Automatic detection and classification of grains of pollen based on shape and texture M Rodriguez-Damian, E Cernadas, A Formella, M Fernandez-Delgado, ... IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and …, 2006 | 106 | 2006 |
| Classification of agricultural soil parameters in India MS Sirsat, E Cernadas, M Fernández-Delgado, R Khan Computers and electronics in agriculture 135, 269-279, 2017 | 103 | 2017 |
| Influence of normalization and color space to color texture classification E Cernadas, M Fernandez-Delgado, E González-Rufino, P Carrión Pattern Recognition 61, 120-138, 2017 | 102 | 2017 |
| Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods MS Sirsat, E Cernadas, M Fernández-Delgado, S Barro Computers and electronics in agriculture 154, 120-133, 2018 | 92 | 2018 |
| Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics E Cernadas, P Carrión, PG Rodríguez, E Muriel, T Antequera Computer Vision and Image Understanding 98 (2), 344-360, 2005 | 81 | 2005 |
| Exhaustive comparison of colour texture features and classification methods to discriminate cells categories in histological images of fish ovary E González-Rufino, P Carrión, E Cernadas, M Fernández-Delgado, ... Pattern Recognition 46 (9), 2391-2407, 2013 | 67 | 2013 |
| Direct Kernel Perceptron (DKP): Ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation M Fernández-Delgado, E Cernadas, S Barro, J Ribeiro, J Neves Neural Networks 50, 60-71, 2014 | 64 | 2014 |
| Pollen classification using brightness-based and shape-based descriptors M Rodriguez-Damian, E Cernadas, A Formella, R Sa-Otero Proceedings of the 17th International Conference on Pattern Recognition …, 2004 | 49 | 2004 |
| Fast support vector classification for large-scale problems Z Akram-Ali-Hammouri, M Fernandez-Delgado, E Cernadas, S Barro IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6184 …, 2021 | 37* | 2021 |
| Recognizing marbling in dry-cured Iberian ham by multiscale analysis E Cernadas, ML Durán, T Antequera Pattern Recognition Letters 23 (11), 1311-1321, 2002 | 36 | 2002 |
| Magnetic resonance imaging as a predictive tool for sensory characteristics and intramuscular fat content of dry‐cured loin T Antequera, E Muriel, PG Rodríguez, E Cernadas, J Ruiz Journal of the Science of Food and Agriculture 83 (4), 268-274, 2003 | 34 | 2003 |
| Computer-aided identification of allergenic species of Urticaceae pollen MP De Sá-otero, A González, M Rodríguez-Damián, E Cernadas Grana 43 (4), 224-230, 2004 | 24 | 2004 |
| Magnetic resonance imaging to classify loin from Iberian pig E Cernadas¹, T Antequera, PG Rodríguez, ML Durán, R Gallardo¹, D Villa¹ Magnetic resonance in food science: a view to the future 262, 239, 2001 | 23 | 2001 |
| Applying active contours to muscle recognition in Iberian ham MRI A Caro, PG Rodríguez, E Cernadas, ML Durán, D Villa IASTED International Conference Signal Processing, Pattern Recognition and …, 2001 | 21 | 2001 |
| Direct parallel perceptrons (DPPs): fast analytical calculation of the parallel perceptrons weights with margin control for classification tasks M Fernández-Delgado, J Ribeiro, E Cernadas, SB Ameneiro IEEE transactions on neural networks 22 (11), 1837-1848, 2011 | 19 | 2011 |
| Magnetic resonance imaging, texture analysis and regression techniques to non-destructively predict the quality characteristics of meat pieces MM Ávila, ML Durán, D Caballero, T Antequera, T Palacios-Pérez, ... Engineering Applications of Artificial Intelligence 82, 110-125, 2019 | 18 | 2019 |
| CT radiomics in colorectal cancer: Detection of KRAS mutation using texture analysis and machine learning V González-Castro, E Cernadas, E Huelga, M Fernández-Delgado, ... Applied Sciences 10 (18), 6214, 2020 | 17 | 2020 |
| Classification of honeybee pollen using a multiscale texture filtering scheme P Carrión, E Cernadas, JF Gálvez, M Damián, P de Sá-Otero Machine Vision and Applications 15, 186-193, 2004 | 17 | 2004 |
