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Luís M. Silva
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2020 – today
- 2024
- [c18]Adriano Gomes
, Luís M. Silva, João Pedro Cruz:
Predicting Employee Attrition in a Multi-company Setting. IDEAL (1) 2024: 337-348 - 2023
- [c17]Nuno M. Santos
Data Management for mobile applications dependent on geo-located data. PaPoC@EuroSys 2023: 70-76 - 2022
- [i3]Luís M. Silva
Geo-located data for better dynamic replication. CoRR abs/2205.01045 (2022)
2010 – 2019
- 2017
- [j5]Chetak Kandaswamy
Multi-source deep transfer learning for cross-sensor biometrics. Neural Comput. Appl. 28(9): 2461-2475 (2017) - [i2]Ricardo Gamelas Sousa, Luís A. Alexandre, Jorge M. Santos
Distribution-Based Categorization of Classifier Transfer Learning. CoRR abs/1712.02159 (2017) - [i1]Ricardo Gamelas Sousa, Jorge M. Santos
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition. CoRR abs/1712.02824 (2017) - 2016
- [c16]Sofia Fernandes, Ricardo Gamelas Sousa, Renato Socodato, Luís M. Silva:
Stacked denoising autoencoders for the automatic recognition of microglial cells' state. ESANN 2016 - 2015
- [c15]Chetak Kandaswamy, Luís M. Silva
Source-Target-Source Classification Using Stacked Denoising Autoencoders. IbPRIA 2015: 39-47 - [c14]Ricardo Gamelas Sousa, Tiago Esteves, Sara Rocha
Automatic Detection of Immunogold Particles from Electron Microscopy Images. ICIAR 2015: 377-384 - [c13]Chetak Kandaswamy, Luís M. Silva
Deep Transfer Learning Ensemble for Classification. IWANN (1) 2015: 335-348 - [c12]Ricardo Gamelas Sousa, Tiago Esteves, Sara Rocha
Transfer Learning for the Recognition of Immunogold Particles in TEM Imaging. IWANN (1) 2015: 374-384 - 2014
- [j4]Luís M. Silva
Classification Performance of Multilayer Perceptrons with Different Risk functionals. Int. J. Pattern Recognit. Artif. Intell. 28(6) (2014) - [c11]Chetak Kandaswamy
Improving Deep Neural Network Performance by Reusing Features Trained with Transductive Transference. ICANN 2014: 265-272 - [c10]Telmo Amaral, Luís M. Silva
Transfer Learning Using Rotated Image Data to Improve Deep Neural Network Performance. ICIAR (1) 2014: 290-300 - [c9]Telmo Amaral, Chetak Kandaswamy, Luís M. Silva
Improving Performance on Problems with Few Labelled Data by Reusing Stacked Auto-Encoders. ICMLA 2014: 367-372 - [c8]Chetak Kandaswamy, Luís M. Silva
Improving transfer learning accuracy by reusing Stacked Denoising Autoencoders. SMC 2014: 1380-1387 - 2013
- [b1]Joaquim Marques de Sá, Luís M. Silva
Minimum Error Entropy Classification. Studies in Computational Intelligence 420, Springer 2013, ISBN 978-3-642-29028-2, pp. 1-213 - [c7]Tânia Fontes
Application of Artificial Neural Networks to Predict the Impact of Traffic Emissions on Human Health. EPIA 2013: 21-29 - [c6]Telmo Amaral, Luís M. Silva
Using Different Cost Functions to Train Stacked Auto-Encoders. MICAI (Special Sessions) 2013: 114-120 - 2011
- [c5]Tânia Fontes
NNIGnets, Neural Networks Software. EANN/AIAI (1) 2011: 345-350 - 2010
- [j3]Luís M. Silva
The MEE Principle in Data Classification: A Perceptron-Based Analysis. Neural Comput. 22(10): 2698-2728 (2010)
2000 – 2009
- 2008
- [j2]Luís M. Silva
Data classification with multilayer perceptrons using a generalized error function. Neural Networks 21(9): 1302-1310 (2008) - [c4]Luís M. Silva
The Influence of the Risk Functional in Data Classification with MLPs. ICANN (1) 2008: 185-194 - 2006
- [j1]Luís M. Silva
Error Entropy in Classification Problems: A Univariate Data Analysis. Neural Comput. 18(9): 2036-2061 (2006) - [c3]Luís M. Silva
New developments of the Z-EDM algorithm. ISDA (1) 2006: 1067-1072 - 2005
- [c2]Luís M. Silva, Joaquim Marques de Sá, Luís A. Alexandre:
Neural network classification using Shannon's entropy. ESANN 2005: 217-222 - [c1]Luís M. Silva, Luís A. Alexandre
Neural Network Classification: Maximizing Zero-Error Density. ICAPR (1) 2005: 127-135
Coauthor Index
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