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Bello, Marilyn
40  Ergebnisse:
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1

ExplainLFS: Explaining neural architectures for similarity ..:

Bello, Marilyn ; Costa, Pablo ; Nápoles, Gonzalo..
Information Fusion.  108 (2024)  - p. 102407 , 2024
Link: https://doi.org/10.1016/..
 
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2

Forward Composition Propagation for Explainable Neural Reas..:

Grau, Isel ; Nápoles, Gonzalo ; Bello, Marilyn..
IEEE Computational Intelligence Magazine.  19 (2024)  1 - p. 26-35 , 2024
Link: https://doi.org/10.1109/..
 
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3

REPROT: Explaining the predictions of complex deep learning..:

Bello, Marilyn ; Nápoles, Gonzalo ; Concepción, Leonardo...
Information Sciences.  654 (2024)  - p. 119851 , 2024
Link: https://doi.org/10.1016/..
 
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4

Explanation of Multi-Label Neural Networks with Layer-Wise ..:

, In: 2022 International Joint Conference on Neural Networks (IJCNN),
Bello, Marilyn ; Napoles, Gonzalo ; Vanhoof, Koen.. - p. 01-10 , 2022
Link: https://doi.org/10.1109/..
 
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5

Layer-Wise Relevance Propagation in Multi-label Neural Netw..:

, In: Progress in Artificial Intelligence and Pattern Recognition; Lecture Notes in Computer Science,
Bello, Marilyn ; Aguilera, Yaumara ; Nápoles, Gonzalo... - p. 3-12 , 2021
Link: https://doi.org/10.1007/..
 
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6

Data quality measures based on granular computing for multi..:

Bello, Marilyn ; Nápoles, Gonzalo ; Vanhoof, Koen.
Information Sciences.  560 (2021)  - p. 51-67 , 2021
Link: https://doi.org/10.1016/..
 
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7

Long-term Cognitive Network-based architecture for multi-la..:

Nápoles, Gonzalo ; Bello, Marilyn ; Salgueiro, Yamisleydi
Neural Networks.  140 (2021)  - p. 39-48 , 2021
Link: https://doi.org/10.1016/..
 
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8

Deep neural network to extract high-level features and labe..:

Bello, Marilyn ; Nápoles, Gonzalo ; Sánchez, Ricardo..
Neurocomputing.  413 (2020)  - p. 259-270 , 2020
Link: https://doi.org/10.1016/..
 
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9

Outliers Detection in Multi-label Datasets:

, In: Advances in Soft Computing; Lecture Notes in Computer Science,
Bello, Marilyn ; Nápoles, Gonzalo ; Morera, Rafael.. - p. 65-75 , 2020
Link: https://doi.org/10.1007/..
 
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10

Feature and Label Association Based on Granulation Entropy ..:

, In: Rough Sets; Lecture Notes in Computer Science,
Bello, Marilyn ; Nápoles, Gonzalo ; Sánchez, Ricardo.. - p. 225-235 , 2020
Link: https://doi.org/10.1007/..
 
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11

Methods to Edit Multi-label Training Sets Using Rough Sets ..:

, In: Rough Sets; Lecture Notes in Computer Science,
Bello, Marilyn ; Nápoles, Gonzalo ; Vanhoof, Koen. - p. 369-380 , 2019
Link: https://doi.org/10.1007/..
 
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12

Fuzzy Activation of Rough Cognitive Ensembles Using OWA Ope..:

, In: Uncertainty Management with Fuzzy and Rough Sets; Studies in Fuzziness and Soft Computing,
Bello, Marilyn ; Nápoles, Gonzalo ; Fuentes, Ivett... - p. 317-335 , 2019
Link: https://doi.org/10.1007/..
 
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13

Prototypes Generation from Multi-label Datasets Based on Gr..:

, In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications; Lecture Notes in Computer Science,
Bello, Marilyn ; Nápoles, Gonzalo ; Vanhoof, Koen. - p. 142-151 , 2019
Link: https://doi.org/10.1007/..
 
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14

Expanding MLkNN Using Extended Rough Set Theory:

, In: Progress in Artificial Intelligence and Pattern Recognition; Lecture Notes in Computer Science,
Pérez, Gabriela ; Bello, Marilyn ; Nápoles, Gonzalo... - p. 247-254 , 2018
Link: https://doi.org/10.1007/..
 
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15

A Solution for the Team Selection Problem Using ACO:

, In: Lecture Notes in Computer Science; Swarm Intelligence,
Lugo, Lázaro ; Bello, Marilyn ; Nowe, Ann. - p. 325-332 , 2018
Link: https://doi.org/10.1007/..
 
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