Publications

2017
Costa JP, Cruz R., Fernandes K., Costa JP, Ortiz MP, Cardoso JS. Combining ranking with traditional methods for ordinal class imbalance. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).; 2017.
2016
Deep Learning and Data Labeling for Medical Applications. Vol 10008. Carneiro G, Mateus D, Peter L et al., editors 2016.
Costa JP, Cruz R., Fernandes K., Cardoso JS, Costa JP. Tackling Class Imbalance with Ranking. In: 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN).; 2016.
2014
Costa JP, Alonso H., Cardoso JS. corrigendum to "the unimodal model for the classification of ordinal data" [neural netw. 21 (2008) 78-79] (doi:10.1016/j.neunet.2007.10.003). neural networks. 2014.
Da Costa J, Alonso H., Cardoso JS. the unimodal model for the classification of ordinal data (vol 21, pg 78, 2008). neural networks. 2014;59:73-75.
Monteiro JP, Oliveira HP, Aguiar P, Cardoso JS. A depth-map approach for automatic mice behavior recognition. In: Image Processing (ICIP), 2014 IEEE International Conference on. IEEE; 2014. 2. p. 2261-2265p.
2013
Sousa R, Yevseyeva I, Da Costa J, Cardoso JS. multicriteria models for learning ordinal data: a literature review. studies in computational intelligence. 2013;427:109-138.
2012
Monteiro JP, Oliveira HP, Aguiar P, Cardoso JS. Depth-map images for automatic mice behavior recognition. In: 1st PhD Students Conference in Electrical and Computer Engineering, Porto, Portugal.; 2012.
2010
Amaral IF, Coelho F., Da Costa J, Cardoso JS. hierarchical medical image annotation using svm-based approaches. proceedings of the ieee/embs region 8 international conference on information technology applications in biomedicine, itab. 2010.
Da Costa JP, Sousa R, Cardoso JS. an all-at-once unimodal svm approach for ordinal classification. proceedings - 9th international conference on machine learning and applications, icmla 2010. 2010:59-64.
Costa JP, Da Costa JP, Sousa R, Cardoso JS. An all-at-once unimodal SVM approach for ordinal classification. In: Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010.; 2010.
Costa JP, Amaral IF, Coelho F., Da Costa J, Cardoso JS. Hierarchical medical image annotation using SVM-based approaches. In: Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB.; 2010.
2009
Cardoso JS, Capela A., Rebelo A, Guedes C., Da Costa J. staff detection with stable paths. ieee transactions on pattern analysis and machine intelligence. 2009;31:1134-1139.
2008
Da Costa J, Alonso H., Cardoso JS. the unimodal model for the classification of ordinal data. neural networks. 2008;21:78-91.
Sousa R, Cardoso JS, Da Costa J, Cardoso M.. breast contour detection with shape priors. 2008 15th ieee international conference on image processing, vols 1-5. 2008:1440-1443.
Costa JP, Da Costa J, Alonso H., Cardoso JS. The unimodal model for the classification of ordinal data. NEURAL NETWORKS. 2008.
2007
Costa JP, Rebelo A, Capela A., Da Costa JP, Guedes C., Carrapatoso E., et al. A shortest path approach for staff line detection. In: AXMEDIS 2007: THIRD INTERNATIONAL CONFERENCE ON AUTOMATED PRODUCTION OF CROSS MEDIA CONTENT FOR MULTI-CHANNEL DISTRIBUTION, PROCEEDINGS.; 2007.
Rebelo A, Capela A., Da Costa JP, Guedes C., Carrapatoso E., Cardoso JS. a shortest path approach for staff line detection. axmedis 2007: third international conference on automated production of cross media content for multi-channel distribution, proceedings. 2007:79-85.
Rebelo A., Capela A., da Costa J., Guedes C., Carrapatoso E., Cardoso JS. A shortest path approach for staff line detection. Delgado J., Ng K, Nesi P., Bellini P., editors 2007.
Cardoso JS, da Costa JF. Learning to classify ordinal data: the data replication method. J. Mach. Learn. Res.. 2007;8:1393-1429.
2005
Cardoso JS, Da Costa J, Cardoso M.. modelling ordinal relations with svms: an application to objective aesthetic evaluation of breast cancer conservative treatment. neural networks. 2005;18:808-817.
Costa JP, Cardoso JS, Da Costa J, Cardoso M.. Modelling ordinal relations with SVMs: An application to objective aesthetic evaluation of breast cancer conservative treatment. NEURAL NETWORKS. 2005.
Costa JP, Cardoso JS, Da Costa J, Cardoso M.. SVMs applied to objective aesthetic evaluation of conservative breast cancer treatment. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5.; 2005.
Da Costa J, Cardoso JS. classification of ordinal data using neural networks. machine learning: ecml 2005, proceedings. 2005;3720:690-697.
Cardoso JS, Da Costa J, Cardoso M.. svms applied to objective aesthetic evaluation of conservative breast cancer treatment. proceedings of the international joint conference on neural networks (ijcnn), vols 1-5. 2005;4:2481-2486.
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