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Julián Luengo-Martín
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2020 – today
- 2024
[j54]Iago Xabier Vázquez
, Bahgat Ayasi, Huseyin Seker, Julián Luengo, Javier Sedano, Ángel Miguel García-Vico:
Combining traditional and spiking neural networks for energy-efficient detection of Eimeria parasites. Appl. Soft Comput. 160: 111681 (2024)- [c32]Ignacio Aguilera-Martos, Andrés Herrera-Poyatos, Julián Luengo, Francisco Herrera:
Local Attention: Enhancing the Transformer Architecture for Efficient Time Series Forecasting. IJCNN 2024: 1-8 - [i10]Iván Sevillano-García, Julián Luengo, Francisco Herrera:
SHIELD: A regularization technique for eXplainable Artificial Intelligence. CoRR abs/2404.02611 (2024) - 2023
- [j53]Iván Sevillano-García, Julián Luengo, Francisco Herrera:
REVEL Framework to Measure Local Linear Explanations for Black-Box Models: Deep Learning Image Classification Case Study. Int. J. Intell. Syst. 2023: 1-34 (2023) - [j52]Ignacio Aguilera-Martos, Ángel Miguel García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera:
TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning. Neurocomputing 517: 223-228 (2023) - [j51]Ignacio Aguilera-Martos, Marta García-Bárzana, Diego García-Gil, Jacinto Carrasco, David López, Julián Luengo, Francisco Herrera:
Multi-step histogram based outlier scores for unsupervised anomaly detection: ArcelorMittal engineering dataset case of study. Neurocomputing 544: 126228 (2023) - [j50]David López, Ignacio Aguilera-Martos, Marta García-Bárzana, Francisco Herrera, Diego García-Gil, Julián Luengo:
Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series. Inf. Fusion 100: 101957 (2023) - [c31]Ignacio Aguilera-Martos, Julián Luengo, Francisco Herrera:
Revisiting Histogram Based Outlier Scores: Strengths and Weaknesses. HAIS 2023: 39-48 - [c30]Iván Sevillano-García, Julián Luengo, Francisco Herrera:
Optimizing LIME Explanations Using REVEL Metrics. HAIS 2023: 304-313 - [c29]Iván Sevillano-García, Julián Luengo, Francisco Herrera:
Low-Impact Feature Reduction Regularization Term: How to Improve Artificial Intelligence with Explainability. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2023: 135-139 - [i9]Adrián Peláez-Vegas, Pablo Mesejo, Julián Luengo:
A Survey on Semi-Supervised Semantic Segmentation. CoRR abs/2302.09899 (2023) - 2022
- [j49]Miriam Seoane Santos, Pedro Henriques Abreu, Alberto Fernández, Julián Luengo, João A. M. Santos:
The impact of heterogeneous distance functions on missing data imputation and classification performance. Eng. Appl. Artif. Intell. 111: 104791 (2022) - [j48]Germán González-Almagro, Juan-Luis Suárez, Julián Luengo, José Ramón Cano, Salvador García:
3SHACC: Three stages hybrid agglomerative constrained clustering. Neurocomputing 490: 441-461 (2022) - [j47]Julián Luengo, Raúl Moreno, Iván Sevillano-García, David Charte, Adrián Peláez-Vegas, Marta Fernández-Moreno, Pablo Mesejo, Francisco Herrera:
A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. Inf. Fusion 78: 232-253 (2022) - [i8]Ignacio Aguilera-Martos, Ángel Miguel García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera:
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study). CoRR abs/2206.03179 (2022) - [i7]Iván Sevillano-García, Julián Luengo-Martín, Francisco Herrera:
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of study. CoRR abs/2211.06154 (2022) - 2021
- [j46]Germán González-Almagro, Julián Luengo, José Ramón Cano, Salvador García:
Enhancing instance-level constrained clustering through differential evolution. Appl. Soft Comput. 108: 107435 (2021) - [j45]Jacinto Carrasco, David López, Ignacio Aguilera-Martos, Diego García-Gil, Irina Markova, Marta García-Bárzana, Manuel Arias-Rodil, Julián Luengo, Francisco Herrera:
Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms. Neurocomputing 462: 440-452 (2021) - [j44]Manuel González, Julián Luengo, José Ramón Cano, Salvador García:
Synthetic Sample Generation for Label Distribution Learning. Inf. Sci. 544: 197-213 (2021) - [j43]Julián Luengo, Dánel Sánchez Tarragó, Ronaldo C. Prati, Francisco Herrera:
Multiple instance classification: Bag noise filtering for negative instance noise cleaning. Inf. Sci. 579: 388-400 (2021) - [j42]Germán González-Almagro, Alejandro Rosales-Pérez, Julián Luengo, José Ramón Cano, Salvador García:
ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism. Swarm Evol. Comput. 66: 100939 (2021) - [i6]Jacinto Carrasco, Irina Markova, David López, Ignacio Aguilera, Diego García, Marta García-Bárzana, Manuel Arias-Rodil, Julián Luengo, Francisco Herrera:
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms. CoRR abs/2105.12818 (2021) - [i5]Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera:
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training. CoRR abs/2109.03748 (2021) - 2020
- [b2]Julián Luengo, Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, Francisco Herrera:
Big Data Preprocessing - Enabling Smart Data. Springer 2020, ISBN 978-3-030-39104-1, pp. 1-186 - [j41]Germán González-Almagro, Julián Luengo, José Ramón Cano, Salvador García:
DILS: Constrained clustering through dual iterative local search. Comput. Oper. Res. 121: 104979 (2020) - [j40]Juan Antonio Cortés-Ibáñez, Sergio González, José Javier Valle-Alonso, Julián Luengo, Salvador García, Francisco Herrera:
Preprocessing methodology for time series: An industrial world application case study. Inf. Sci. 514: 385-401 (2020) - [j39]Jesús Maillo, Salvador García, Julián Luengo, Francisco Herrera, Isaac Triguero:
Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data. IEEE Trans. Fuzzy Syst. 28(5): 874-886 (2020) - [j38]Siham Tabik, Anabel Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, Manuel Rey-Area, David Charte, Emilio Guirado, Juan-Luis Suárez, Julián Luengo, M. A. Valero-González, P. García-Villanova, Eulalia Olmedo-Sánchez, Francisco Herrera:
COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. IEEE J. Biomed. Health Informatics 24(12): 3595-3605 (2020) - [c28]Germán González-Almagro, Alejandro Rosales-Pérez, Julián Luengo, José Ramón Cano, Salvador García:
Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. GECCO 2020: 333-341 - [c27]Germán González-Almagro, Juan-Luis Suárez, Julián Luengo, José Ramón Cano, Salvador García:
Agglomerative Constrained Clustering Through Similarity and Distance Recalculation. HAIS 2020: 424-436 - [c26]José Ramón Cano, Julián Luengo, Salvador García:
Similarity-based and Iterative Label Noise Filters for Monotonic Classification. HICSS 2020: 1-9 - [i4]Siham Tabik, Anabel Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, Manuel Rey-Area, David Charte, Emilio Guirado, Juan-Luis Suárez, Julián Luengo, M. A. Valero-González, P. García-Villanova, Eulalia Olmedo-Sánchez, Francisco Herrera:
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images. CoRR abs/2006.01409 (2020)
2010 – 2019
- 2019
- [j37]Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Bartosz Krawczyk, Francisco Herrera:
Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. Expert Syst. Appl. 118: 315-328 (2019) - [j36]Diego García-Gil, Francisco Luque Sánchez, Julián Luengo, Salvador García, Francisco Herrera:
From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification. Int. J. Intell. Syst. 34(12): 3260-3274 (2019) - [j35]José Ramón Cano, Julián Luengo, Salvador García:
Label noise filtering techniques to improve monotonic classification. Neurocomputing 353: 83-95 (2019) - [j34]Ignacio Cordón, Julián Luengo, Salvador García, Francisco Herrera, Francisco Charte:
Smartdata: Data preprocessing to achieve smart data in R. Neurocomputing 360: 1-13 (2019) - [j33]Diego García-Gil, Julián Luengo, Salvador García, Francisco Herrera:
Enabling Smart Data: Noise filtering in Big Data classification. Inf. Sci. 479: 135-152 (2019) - [j32]Ronaldo C. Prati, Julián Luengo, Francisco Herrera:
Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. Knowl. Inf. Syst. 60(1): 63-97 (2019) - [j31]Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Francisco Herrera:
Coral species identification with texture or structure images using a two-level classifier based on Convolutional Neural Networks. Knowl. Based Syst. 184 (2019) - [j30]Isaac Triguero, Diego García-Gil, Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera:
Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data. WIREs Data Mining Knowl. Discov. 9(2) (2019) - [c25]Besay Montesdeoca, Julián Luengo, Jesús Maillo, Diego García-Gil, Salvador García, Francisco Herrera:
A First Approach on Big Data Missing Values Imputation. IoTBDS 2019: 315-323 - [c24]Diego García-Gil, Alejandro Alcalde-Barros, Julián Luengo, Salvador García, Francisco Herrera:
Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries. IoTBDS 2019: 324-331 - 2018
- [j29]Julián Luengo, Seong-O Shim, Saleh Alshomrani, Abdulrahman H. Altalhi, Francisco Herrera:
CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring. Knowl. Based Syst. 140: 27-49 (2018) - [c23]Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera, Isaac Triguero:
A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification. FUZZ-IEEE 2018: 1-8 - [c22]Julián Luengo, Dánel Sánchez Tarragó, Ronaldo C. Prati, Francisco Herrera:
A First Study on the Use of Noise Filtering to Clean the Bags in Multi-Instance Classification. LOPAL 2018: 3:1-3:6 - [i3]Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Bartosz Krawczyk, Francisco Herrera:
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation. CoRR abs/1804.00516 (2018) - [i2]José Ramón Cano, Julián Luengo, Salvador García:
Label Noise Filtering Techniques to Improve Monotonic Classification. CoRR abs/1810.08914 (2018) - 2017
- [j28]Isaac Triguero, Sergio González, Jose M. Moyano, Salvador García, Jesús Alcalá-Fdez, Julián Luengo, Alberto Fernández, María José del Jesus, Luciano Sánchez, Francisco Herrera:
KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining. Int. J. Comput. Intell. Syst. 10(1): 1238-1249 (2017) - [j27]Pablo Morales-Álvarez, Julián Luengo, Luís Paulo F. Garcia, Ana Carolina Lorena, André C. P. L. F. de Carvalho, Francisco Herrera:
The NoiseFiltersR Package: Label Noise Preprocessing in R. R J. 9(1): 219 (2017) - [c21]Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera, Isaac Triguero:
Exact fuzzy k-nearest neighbor classification for big datasets. FUZZ-IEEE 2017: 1-6 - [c20]Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera:
A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost. HAIS 2017: 268-280 - [i1]Diego García-Gil, Julián Luengo, Salvador García, Francisco Herrera:
Enabling Smart Data: Noise filtering in Big Data classification. CoRR abs/1704.01770 (2017) - 2016
- [j26]José A. Sáez, Julián Luengo, Francisco Herrera:
Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure. Neurocomputing 176: 26-35 (2016) - [j25]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Inf. Fusion 27: 19-32 (2016) - [j24]Salvador García, Julián Luengo, Francisco Herrera:
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowl. Based Syst. 98: 1-29 (2016) - [j23]Julián Luengo, Ángel Miguel García-Vico, M. Dolores Pérez-Godoy, Cristóbal J. Carmona:
The influence of noise on the evolutionary fuzzy systems for subgroup discovery. Soft Comput. 20(11): 4313-4330 (2016) - [c19]Pablo Morales-Alvarez, Julián Luengo, Francisco Herrera:
A First Study on the Use of Boosting for Class Noise Reparation. HAIS 2016: 549-559 - [c18]Isaac Triguero, Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera:
From Big Data to Smart Data with the K-Nearest Neighbours Algorithm. iThings/GreenCom/CPSCom/SmartData 2016: 859-864 - 2015
- [b1]Salvador García, Julián Luengo, Francisco Herrera:
Data Preprocessing in Data Mining. Intelligent Systems Reference Library 72, Springer 2015, ISBN 978-3-319-10246-7, pp. 1-313 - [j22]José A. Sáez, Julián Luengo, Jerzy Stefanowski, Francisco Herrera:
SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf. Sci. 291: 184-203 (2015) - [j21]Julián Luengo, Francisco Herrera:
An automatic extraction method of the domains of competence for learning classifiers using data complexity measures. Knowl. Inf. Syst. 42(1): 147-180 (2015) - [j20]Luís Paulo F. Garcia, José A. Sáez, Julián Luengo, Ana Carolina Lorena, André C. P. L. F. de Carvalho, Francisco Herrera:
Using the One-vs-One decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowl. Based Syst. 90: 153-164 (2015) - [c17]Julián Luengo, Rafael Rumí:
Naive Bayes Classifier with Mixtures of Polynomials. ICPRAM (1) 2015: 14-24 - [c16]Cristóbal J. Carmona, Julián Luengo:
A First Approach in the Class Noise Filtering Approaches for Fuzzy Subgroup Discovery. SOCO 2015: 387-399 - 2014
- [j19]Isaac Triguero, José A. Sáez, Julián Luengo, Salvador García, Francisco Herrera:
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. Neurocomputing 132: 30-41 (2014) - [j18]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition. Knowl. Inf. Syst. 38(1): 179-206 (2014) - [j17]José A. Sáez, Joaquín Derrac, Julián Luengo, Francisco Herrera:
Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers. Pattern Recognit. 47(12): 3941-3948 (2014) - [c15]José A. Sáez, Joaquín Derrac, Julián Luengo, Francisco Herrera:
Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes. HAIS 2014: 597-606 - [c14]José A. Sáez, Julián Luengo, Jerzy Stefanowski, Francisco Herrera:
Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering. IDEAL 2014: 61-68 - 2013
- [j16]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness. Inf. Sci. 247: 1-20 (2013) - [j15]José A. Sáez, Julián Luengo, Francisco Herrera:
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognit. 46(1): 355-364 (2013) - [j14]Salvador García, Julián Luengo, José Antonio Sáez, Victoria López, Francisco Herrera:
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. IEEE Trans. Knowl. Data Eng. 25(4): 734-750 (2013) - [c13]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
An Experimental Case of Study on the Behavior of Multiple Classifier Systems with Class Noise Datasets. HAIS 2013: 568-577 - 2012
- [j13]Cristóbal J. Carmona, Julián Luengo, Pedro González, María José del Jesus:
An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery. Expert Syst. Appl. 39(13): 11404-11412 (2012) - [j12]Julián Luengo, Francisco Herrera:
Shared domains of competence of approximate learning models using measures of separability of classes. Inf. Sci. 185(1): 43-65 (2012) - [j11]Julián Luengo, Salvador García, Francisco Herrera:
On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowl. Inf. Syst. 32(1): 77-108 (2012) - [j10]Julián Luengo, José A. Sáez, Francisco Herrera:
Missing data imputation for fuzzy rule-based classification systems. Soft Comput. 16(5): 863-881 (2012) - [c12]Cristóbal J. Carmona, Julián Luengo, Pedro González, María José del Jesus:
A preliminary study on missing data imputation in evolutionary fuzzy systems of subgroup discovery. FUZZ-IEEE 2012: 1-7 - [c11]José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera:
A First Study on Decomposition Strategies with Data with Class Noise Using Decision Trees. HAIS (2) 2012: 25-35 - [c10]Salvador García, Victoria López, Julián Luengo, Cristóbal J. Carmona, Francisco Herrera:
A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms. ICPRAM (1) 2012: 211-216 - 2011
- [j9]Salvador García, Joaquín Derrac, Julián Luengo, Cristóbal J. Carmona, Francisco Herrera:
Evolutionary selection of hyperrectangles in nested generalized exemplar learning. Appl. Soft Comput. 11(3): 3032-3045 (2011) - [j8]Jesús Alcalá-Fdez, Alberto Fernández, Julián Luengo, Joaquín Derrac, Salvador García:
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. J. Multiple Valued Log. Soft Comput. 17(2-3): 255-287 (2011) - [j7]Julián Luengo, Alberto Fernández, Salvador García, Francisco Herrera:
Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling. Soft Comput. 15(10): 1909-1936 (2011) - [c9]José A. Sáez, Julián Luengo, Francisco Herrera:
Fuzzy Rule Based Classification Systems versus crisp robust learners trained in presence of class noise's effects: A case of study. ISDA 2011: 1229-1234 - 2010
- [j6]Julián Luengo, Francisco Herrera:
Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method. Fuzzy Sets Syst. 161(1): 3-19 (2010) - [j5]Salvador García, Alberto Fernández, Julián Luengo, Francisco Herrera:
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180(10): 2044-2064 (2010) - [j4]Julián Luengo, Salvador García, Francisco Herrera:
A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and EventCovering method. Neural Networks 23(3): 406-418 (2010) - [j3]Alberto Fernández, Salvador García, Julián Luengo, Ester Bernadó-Mansilla, Francisco Herrera:
Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study. IEEE Trans. Evol. Comput. 14(6): 913-941 (2010) - [c8]Julián Luengo, Francisco Herrera:
An extraction method for the characterization of the Fuzzy Rule Based Classification Systems' behavior using data complexity measures: A case of study with FH-GBML. FUZZ-IEEE 2010: 1-8 - [c7]José A. Sáez, Julián Luengo, Francisco Herrera:
A first study on the noise impact in classes for Fuzzy Rule Based Classification Systems. ISKE 2010: 153-158
2000 – 2009
- 2009
- [j2]Julián Luengo, Salvador García, Francisco Herrera:
A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests. Expert Syst. Appl. 36(4): 7798-7808 (2009) - [j1]Salvador García, Alberto Fernández, Julián Luengo, Francisco Herrera:
A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13(10): 959-977 (2009) - [c6]Julián Luengo, Francisco Herrera:
On the use of Measures of Separability of Classes to Characterise the Domains of Competence of a Fuzzy Rule Based Classification System. IFSA/EUSFLAT Conf. 2009: 1027-1032 - [c5]Alberto Fernández, Julián Luengo, Joaquín Derrac, Jesús Alcalá-Fdez, Francisco Herrera:
Implementation and Integration of Algorithms into the KEEL Data-Mining Software Tool. IDEAL 2009: 562-569 - [c4]Salvador García, Joaquín Derrac, Julián Luengo, Francisco Herrera:
A First Approach to Nearest Hyperrectangle Selection by Evolutionary Algorithms. ISDA 2009: 517-522 - [c3]Julián Luengo, Alberto Fernández, Salvador García, Francisco Herrera:
Addressing Data-Complexity for Imbalanced Data-Sets: A Preliminary Study on the Use of Preprocessing for C4.5. ISDA 2009: 523-528 - [c2]Julián Luengo, Francisco Herrera:
Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes. IWANN (1) 2009: 81-88 - 2007
- [c1]Julián Luengo, Salvador García, Francisco Herrera:
A Study on the Use of Statistical Tests for Experimentation with Neural Networks. IWANN 2007: 72-79
Coauthor Index
[c32] [i10] [j53] [j52] [j51] [j50] [c31] [c30] [c29] [j47] [i8] [i7] [j45] [j43] [i6] [i5] [b2] [j40] [j39] [j38] [i4] [j37] [j36] [j34] [j33] [j32] [j31] [j30] [c25] [c24] [j29] [c23] [c22] [i3] [j28] [j27] [c21] [c20] [i1] [j26] [j25] [j24] [c19] [c18] [b1] [j22] [j21] [j20] [j19] [j18] [j17] [c15] [c14] [j16] [j15] [j14] [c13] [j12] [j11] [j10] [c11] [c10] [j9] [j7] [c9] [j6] [j5] [j4] [j3] [c8] [c7] [j2] [j1] [c6] [c5] [c4] [c3] [c2] [c1]
José A. Sáez
aka: José Antonio Sáez
aka: José Antonio Sáez
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