Publicacions en col·laboració amb investigadors/es de Universidad Carlos III de Madrid (29)

2022

  1. Double-Layer Stacked Denoising Autoencoders for Regression

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2021

  1. Complete Stacked Denoising Auto-Encoders for Regression

    Neural Processing Letters, Vol. 53, Núm. 1, pp. 787-797

  2. Complete autoencoders for classification with missing values

    Neural Computing and Applications, Vol. 33, Núm. 6, pp. 1951-1957

2020

  1. Designing non-linear minimax and related discriminants by disjoint tangent configurations applied to RBF networks

    Neurocomputing, Vol. 383, pp. 106-112

  2. Improving deep learning performance with missing values via deletion and compensation

    Neural Computing and Applications, Vol. 32, Núm. 17, pp. 13233-13244

  3. MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1% error rate. ensembles overview and proposal

    Information Fusion, Vol. 62, pp. 73-80

2019

  1. Exploiting label information to improve auto-encoding based classifiers

    Neurocomputing, Vol. 370, pp. 104-108

  2. Machine-Health Application Based on Machine Learning Techniques for Prediction of Valve Wear in a Manufacturing Plant

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Machine-health application based on machine learning techniques for prediction of valve wear in a manufacturing plant

    From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II (Springer Suiza), pp. 389-398

  4. On improving CNNs performance: The case of MNIST

    Information Fusion

2018

  1. Linear discriminants described by disjoint tangent configurations

    Neurocomputing, Vol. 316, pp. 345-356

2017

  1. Values deletion to improve deep imputation processes

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2013

  1. Classifying patterns with missing values using Multi-Task Learning perceptrons

    Expert Systems with Applications, Vol. 40, Núm. 4, pp. 1333-1341

2010

  1. Pattern classification with missing data: A review

    Neural Computing and Applications, Vol. 19, Núm. 2, pp. 263-282

2009

  1. Classification with incomplete data

    Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (IGI Global), pp. 147-175

  2. Combining missing data imputation and pattern classification in a multi-layer perceptron

    Intelligent Automation and Soft Computing, Vol. 15, Núm. 4, pp. 539-553

  3. K nearest neighbours with mutual information for simultaneous classification and missing data imputation

    Neurocomputing, Vol. 72, Núm. 7-9, pp. 1483-1493

2008

  1. A robust approach for classifying unknown data in medical diagnosis problems

    2008 World Automation Congress, WAC 2008

  2. Incomplete pattern classification using a multi-task approach

    WMSCI 2008 - The 12th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 14th International Conference on Information Systems Analysis and Synthesis, ISAS 2008 - Proc.

  3. K-nearest neighbours based on mutual information for incomplete data classification

    ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning