Jorge M. Santos

According to our database, Jorge M. Santos authored at least 25 papers between 2004 and 2017.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2017

Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition.

Ricardo Gamelas Sousa, Jorge M. Santos, Luís M. Silva, Luís A. Alexandre, Tiago Esteves, Sara Rocha, Paulo Monjardino, Joaquim Marques de Sá, Francisco Figueiredo, Pedro Quelhas

CoRR, 2017

Distribution-Based Categorization of Classifier Transfer Learning.

Ricardo Gamelas Sousa, Luís A. Alexandre, Jorge M. Santos, Luís M. Silva, Joaquim Marques de Sá

CoRR, 2017

2015

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus.

Manuel B. Cruz, Jorge M. Santos, Nuno Cruz

Appl. Intell., 2015

Transfer Learning for the Recognition of Immunogold Particles in TEM Imaging.

Ricardo Gamelas Sousa, Tiago Esteves, Sara Rocha, Francisco Figueiredo, Joaquim Marques de Sá, Luís A. Alexandre, Jorge M. Santos, Luís M. Silva

Proceedings of the Advances in Computational Intelligence, 2015

Deep Transfer Learning Ensemble for Classification.

Chetak Kandaswamy, Luís M. Silva, Luís A. Alexandre, Jorge M. Santos

Proceedings of the Advances in Computational Intelligence, 2015

2014

Classification Performance of Multilayer Perceptrons with Different Risk functionals.

Luís M. Silva, Jorge M. Santos, Joaquim Marques de Sá

Int. J. Pattern Recognit. Artif. Intell., 2014

Improving transfer learning accuracy by reusing Stacked Denoising Autoencoders.

Chetak Kandaswamy, Luís M. Silva, Luís A. Alexandre, Ricardo Gamelas Sousa, Jorge M. Santos, Joaquim Marques de Sá

Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics, 2014

A Family of Two-Dimensional Benchmark Data Sets and Its Application to Comparing Different Cluster Validation Indices.

Jorge M. Santos, Mark J. Embrechts

Proceedings of the Pattern Recognition - 6th Mexican Conference, 2014

Improving Performance on Problems with Few Labelled Data by Reusing Stacked Auto-Encoders.

Telmo Amaral, Chetak Kandaswamy, Luís M. Silva, Luís A. Alexandre, Joaquim Marques de Sá, Jorge M. Santos

Proceedings of the 13th International Conference on Machine Learning and Applications, 2014

Transfer Learning Using Rotated Image Data to Improve Deep Neural Network Performance.

Telmo Amaral, Luís M. Silva, Luís A. Alexandre, Chetak Kandaswamy, Joaquim Marques de Sá, Jorge M. Santos

Proceedings of the Image Analysis and Recognition - 11th International Conference, 2014

Improving Deep Neural Network Performance by Reusing Features Trained with Transductive Transference.

Chetak Kandaswamy, Luís M. Silva, Luís A. Alexandre, Jorge M. Santos, Joaquim Marques de Sá

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

2013

Minimum Error Entropy Classification

Joaquim Marques de Sá, Luís M. Silva, Jorge M. Santos, Luís A. Alexandre

Studies in Computational Intelligence 420, Springer, ISBN: 978-3-642-29028-2, 2013

Evaluating Entropic Based Clustering Algorithms on Biomedical Data.

Jorge M. Santos, Frederico Morais

Proceedings of the 12th Mexican International Conference on Artificial Intelligence, 2013

Using Different Cost Functions to Train Stacked Auto-Encoders.

Telmo Amaral, Luís M. Silva, Luís A. Alexandre, Chetak Kandaswamy, Jorge M. Santos, Joaquim Marques de Sá

Proceedings of the 12th Mexican International Conference on Artificial Intelligence, 2013

Estimating the Maximum Shear Modulus with Neural Networks.

Manuel B. Cruz, Jorge M. Santos, Nuno Cruz

Proceedings of the Recent Trends in Applied Artificial Intelligence, 2013

Random Brains: An ensemble method for feature selection with neural networks.

Mark J. Embrechts, Jonathan D. Linton, Jorge M. Santos

Proceedings of the 21st European Symposium on Artificial Neural Networks, 2013

2011

NNIGnets, Neural Networks Software.

Tânia Fontes, Vânia Lopes, Luís M. Silva, Jorge M. Santos, Joaquim Marques de Sá

Proceedings of the Engineering Applications of Neural Networks, 2011

Maximum Shear Modulus Prediction by Marchetti Dilatometer Test Using Neural Networks.

Manuel B. Cruz, Jorge M. Santos, Nuno Cruz

Proceedings of the Engineering Applications of Neural Networks, 2011

2010

Using a clustering similarity measure for feature selection in high dimensional data sets.

Jorge M. Santos, Sandra Ramos

Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, 2010

2009

On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification.

Jorge M. Santos, Mark J. Embrechts

Proceedings of the Artificial Neural Networks, 2009

2008

LEGClust - A Clustering Algorithm Based on Layered Entropic Subgraphs.

Jorge M. Santos, Joaquim Marques de Sá, Luís A. Alexandre

IEEE Trans. Pattern Anal. Mach. Intell., 2008

The Influence of the Risk Functional in Data Classification with MLPs.

Luís M. Silva, Mark J. Embrechts, Jorge M. Santos, Joaquim Marques de Sá

Proceedings of the Artificial Neural Networks, 2008

2006

Modular Neural Network Task Decomposition Via Entropic Clustering.

Jorge M. Santos, Luís A. Alexandre, Joaquim Marques de Sá

Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA 2006), 2006

2005

Batch-Sequential Algorithm for Neural Networks Trained with Entropic Criteria.

Jorge M. Santos, Joaquim Marques de Sá, Luís A. Alexandre

Proceedings of the Artificial Neural Networks: Formal Models and Their Applications, 2005

2004

Neural Network Classification Using Error Entropy Minimization.

Jorge M. Santos, Luís A. Alexandre, Joaquim Marques de Sá

Proceedings of the Biological and Artificial Intelligence Environments, 2004