FERMÍN
SEGOVIA ROMÁN
PROFESOR TITULAR DE UNIVERSIDAD


University of Cambridge
Cambridge, Reino UnidoPublications in collaboration with researchers from University of Cambridge (15)
2023
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Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
Information Fusion, Vol. 100
2022
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A Connection between Pattern Classification by Machine Learning and Statistical Inference with the General Linear Model
IEEE Journal of Biomedical and Health Informatics, Vol. 26, Núm. 11, pp. 5332-5343
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Standardization of Agnostic Learning Techniques in Neuroimaging: a Case Study in EEG
2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
2021
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Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities
Information Fusion, Vol. 66, pp. 198-212
2020
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Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications
Neurocomputing, Vol. 410, pp. 237-270
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Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by machine learning
Information Fusion, Vol. 58, pp. 153-167
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Granger causality-based information fusion applied to electrical measurements from power transformers
Information Fusion, Vol. 57, pp. 59-70
2019
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A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms
International Journal of Neural Systems, Vol. 29, Núm. 7
2018
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Assessing mild cognitive impairment progression using a spherical brain mapping of magnetic resonance imaging
Journal of Alzheimer's Disease, Vol. 65, Núm. 3, pp. 713-729
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Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares
Journal of Neuroscience Methods, Vol. 302, pp. 47-57
2017
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A heavy tailed expectation maximization hidden markov random field model with applications to segmentation of MRI
Frontiers in Neuroinformatics, Vol. 11
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A semi-supervised learning approach for model selection based on class-hypothesis testing
Expert Systems with Applications, Vol. 90, pp. 40-49
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Case-Based Statistical Learning: A Non-Parametric Implementation with a Conditional-Error Rate SVM
IEEE Access, Vol. 5, pp. 11468-11478
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Preprocessing of 18F-DMFP-PET data based on Hidden Markov random fields and the Gaussian distribution
Frontiers in Aging Neuroscience, Vol. 9, Núm. OCT
2014
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Identifying endophenotypes of autism: A multivariate approach
Frontiers in Computational Neuroscience, Vol. 8, Núm. JUN