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Oscar Luaces
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- affiliation: University of Oviedo, Spain
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2020 – today
- 2024
- [j34]Gonzalo de la Cruz
, Madalena Lira
Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection. IEEE Trans. Neural Networks Learn. Syst. 35(4): 5130-5140 (2024) - 2023
- [j33]Pablo Pérez-Núñez
Users' photos of items can reveal their tastes in a recommender system. Inf. Sci. 642: 119227 (2023) - [j32]Pablo Pérez-Núñez
All-in-one picture: visual summary of items in a recommender system. Neural Comput. Appl. 35(27): 20339-20349 (2023) - 2022
- [j31]Pablo Pérez-Núñez
User encoding for clustering in very sparse recommender systems tasks. Multim. Tools Appl. 81(2): 2467-2488 (2022) - [j30]Pablo Pérez-Núñez, Jorge Díez
Correction to: User encoding for clustering in very sparse recommender systems tasks. Multim. Tools Appl. 81(23): 34165-34166 (2022) - 2020
- [j29]Jorge Díez
Towards explainable personalized recommendations by learning from users' photos. Inf. Sci. 520: 416-430 (2020) - [j28]Pablo Pérez-Núñez
Improving recommender systems by encoding items and user profiles considering the order in their consumption history. Prog. Artif. Intell. 9(1): 67-75 (2020)
2010 – 2019
- 2019
- [j27]Jorge Díez
Optimizing novelty and diversity in recommendations. Prog. Artif. Intell. 8(1): 101-109 (2019) - 2018
- [j26]Oscar Luaces
A peer assessment method to provide feedback, consistent grading and reduce students' burden in massive teaching settings. Comput. Educ. 126: 283-295 (2018) - [j25]Jaime Alonso
A new method to learn growth curves of beef cattle using a factorization approach. Comput. Electron. Agric. 151: 77-83 (2018) - [j24]Shu-Chao Pang
Deep Learning and Preference Learning for Object Tracking: A Combined Approach. Neural Process. Lett. 47(3): 859-876 (2018) - 2017
- [j23]Shu-Chao Pang
Deep learning to frame objects for visual target tracking. Eng. Appl. Artif. Intell. 65: 406-420 (2017) - [j22]Oscar Luaces
Content-based methods in peer assessment of open-response questions to grade students as authors and as graders. Knowl. Based Syst. 117: 79-87 (2017) - [c18]Verónica Bolón-Canedo, Jorge Díez, Oscar Luaces
Paving the way for providing teaching feedback in automatic evaluation of open response assignments. IJCNN 2017: 3447-3453 - 2016
- [j21]Jorge Díez, Juan José del Coz
Using tensor products to detect unconditional label dependence in multilabel classifications. Inf. Sci. 329: 20-32 (2016) - [j20]Enrique J. deAndrés-Galiana
Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems. J. Biomed. Informatics 60: 342-351 (2016) - [c17]Shu-Chao Pang
Combining Deep Learning and Preference Learning for Object Tracking. ICONIP (3) 2016: 70-77 - [e1]Oscar Luaces, José A. Gámez, Edurne Barrenechea, Alicia Troncoso, Mikel Galar, Héctor Quintián, Emilio Corchado:
Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Salamanca, Spain, September 14-16, 2016. Proceedings. Lecture Notes in Computer Science 9868, Springer 2016, ISBN 978-3-319-44635-6 [contents] - 2015
- [j19]Oscar Luaces
Mapping preferences into Euclidean space. Expert Syst. Appl. 42(22): 8588-8596 (2015) - [j18]Oscar Luaces
A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessments. Knowl. Based Syst. 85: 322-328 (2015) - [j17]Jorge Díez, Oscar Luaces
Optimizing different loss functions in multilabel classifications. Prog. Artif. Intell. 3(2): 107-118 (2015) - [j16]Jorge Díez, Edna Gamboa, Teresita González de Cossío, Oscar Luaces
Analysis of nutrition data by means of a matrix factorization method. Prog. Artif. Intell. 3(3-4): 119-127 (2015) - [c16]Oscar Luaces
Including Content-Based Methods in Peer-Assessment of Open-Response Questions. ICDM Workshops 2015: 273-279 - 2014
- [j15]Gerardo Lastra, Oscar Luaces
Interval prediction for graded multi-label classification. Pattern Recognit. Lett. 49: 171-176 (2014) - 2013
- [j14]José Antonio Sánchez del Rivero, Elena Montañés-Roces
Feature selection for classification of animal feed ingredients from near infrared microscopy spectra. Inf. Sci. 241: 58-69 (2013) - 2012
- [j13]Oscar Luaces
Binary relevance efficacy for multilabel classification. Prog. Artif. Intell. 1(4): 303-313 (2012) - [j12]José Ramón Quevedo, Oscar Luaces
Multilabel classifiers with a probabilistic thresholding strategy. Pattern Recognit. 45(2): 876-883 (2012) - [j11]José Ramón Quevedo, Antonio Bahamonde, Miguel Pérez-Enciso
Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option. IEEE ACM Trans. Comput. Biol. Bioinform. 9(1): 88-97 (2012) - 2011
- [j10]Oscar Luaces
Using nondeterministic learners to alert on coffee rust disease. Expert Syst. Appl. 38(11): 14276-14283 (2011) - [c15]Gerardo Lastra, Oscar Luaces
Graphical Feature Selection for Multilabel Classification Tasks. IDA 2011: 246-257 - 2010
- [j9]Oscar Luaces
Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models. J. Comput. Biol. 17(12): 1711-1723 (2010) - [c14]Oscar Luaces
Viability of an Alarm Predictor for Coffee Rust Disease Using Interval Regression. IEA/AIE (2) 2010: 337-346 - [c13]José Ramón Quevedo, Elena Montañés
Adapting Decision DAGs for Multipartite Ranking. ECML/PKDD (3) 2010: 115-130
2000 – 2009
- 2009
- [j8]Oscar Luaces
Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples. Artif. Intell. Medicine 45(1): 63-76 (2009) - [c12]Antonio Bahamonde, Jaime Alonso
Prediction and Inheritance of Phenotypes. IWINAC (1) 2009: 275-284 - [c11]Jorge Díez, Juan José del Coz
Soft Margin Trees. ECML/PKDD (1) 2009: 302-314 - 2008
- [j7]Jorge Díez, Juan José del Coz
Clustering people according to their preference criteria. Expert Syst. Appl. 34(2): 1274-1284 (2008) - [c10]Jaime Alonso
Learning to Predict One or More Ranks in Ordinal Regression Tasks. ECML/PKDD (1) 2008: 39-54 - 2007
- [j6]José Ramón Quevedo, Antonio Bahamonde, Oscar Luaces
A simple and efficient method for variable ranking according to their usefulness for learning. Comput. Stat. Data Anal. 52(1): 578-595 (2007) - [c9]Oscar Luaces, José Ramón Quevedo, Francisco Taboada, Guillermo M. Albaiceta, Antonio Bahamonde:
Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve. IJCAI 2007: 956-961 - 2004
- [c8]Jorge Díez, Gustavo F. Bayón, José Ramón Quevedo, Juan José del Coz, Oscar Luaces, Jaime Alonso, Antonio Bahamonde:
Discovering Relevancies in Very Difficult Regression Problems: Applications to Sensory Data Analysis. ECAI 2004: 993-994 - [c7]Oscar Luaces, Gustavo F. Bayón, José Ramón Quevedo, Jorge Díez, Juan José del Coz, Antonio Bahamonde:
Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection. ECML 2004: 286-297 - [c6]Antonio Bahamonde, Gustavo F. Bayón, Jorge Díez, José Ramón Quevedo, Oscar Luaces, Juan José del Coz, Jaime Alonso
Feature subset selection for learning preferences: a case study. ICML 2004 - [c5]Juan José del Coz, Gustavo F. Bayón, Jorge Díez, Oscar Luaces, Antonio Bahamonde, Carlos Sañudo:
Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM. NIPS 2004: 321-328 - 2003
- [j5]José Ranilla, Oscar Luaces, Antonio Bahamonde:
A heuristic for learning decision trees and pruning them into classification rules. AI Commun. 16(2): 71-87 (2003) - [j4]Oscar Luaces
Inflating examples to obtain rules. Int. J. Intell. Syst. 18(11): 1113-1143 (2003) - 2002
- [j3]Jorge Díez Peláez, José Ranilla, Oscar Luaces:
Aplicacion de un proceso de seleccion de reglas a un sistema de aprendizaje. Inteligencia Artif. 6(15) (2002) - [c4]Jorge Díez, Juan José del Coz, Oscar Luaces
Learning to Assess from Pair-Wise Comparisons. IBERAMIA 2002: 481-490 - 2000
- [j2]Oscar Luaces, Antonio Bahamonde:
Aprendizaje de la Similitud entre Casos con Valores Discretos y Numericos (Premio Accésit Jose Cuena). Inteligencia Artif. 4(9): 38-44 (2000) - [j1]F. López, Félix Goyache, José Ramón Quevedo, Jaime Alonso, José Ranilla, Oscar Luaces, Antonio Bahamonde, Juan José del Coz:
Un sistema inteligente para calificar morfológicamente a bovinos de la raza Asturiana de los Valles. Inteligencia Artif. 4(10): 5-17 (2000)
1990 – 1999
- 1999
- [c3]Oscar Luaces
Autonomous Clustering for Machine Learning. IWANN (1) 1999: 497-506 - [c2]Juan José del Coz
Self-Organizing Cases to Find Paradigms. IWANN (1) 1999: 527-536 - 1998
- [c1]Oscar Luaces
Machine Learning Usefulness Relies on Accuracy and Self-Maintenance. IEA/AIE (Vol. 2) 1998: 448-457
Coauthor Index
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