E. Cernadas
Cited by
Cited by
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
An extensive experimental survey of regression methods
M Fernández-Delgado, MS Sirsat, E Cernadas, S Alawadi, S Barro, ...
Neural Networks 111, 11-34, 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
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
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
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
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
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
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
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
Recognizing marbling in dry-cured Iberian ham by multiscale analysis
E Cernadas, ML Durán, T Antequera
Pattern Recognition Letters 23 (11), 1311-1321, 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
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
Fast support vector classification for large-scale problems
Z Akram-Ali-Hammouri, M Fernández-Delgado, E Cernadas, S Barro
IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6184 …, 2021
Magnetic resonance imaging to classify loin from Iberian pig
E Cernadasš, T Antequera, PG Rodríguez, ML Duran, R Gallardoš, D Villaš
Magnetic resonance in food science: a view to the future 262, 239, 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
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
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
Improved classification of pollen texture images using SVM and MLP
M Fernandez-Delgado, P Carrion, E Cernadas, JF Galvez, P Sa-Otero
3rd IASTED International Conference on Visualization, Imaging and Image …, 2003
Automatic identification and classification of pollen of the urticaceae family
M Rodriguez-Damian, E Cernadas, A Formella, A González
Proceedings of Advanced Concepts for Intelligent Vision Systems (ACIVS 2003 …, 2003
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