Related Links

Publications

Most of my publications are accesible at my Google Scholar Profile

You can also check my DBLP list

And also this (not always updated) list.

Journals:

  • 1. Predictions of the maximum amplitude for Solar Cycle 23 and its subsequent behavior using nonlinear methods, P. F. Verdes, M. A. Parodi, P. M. Granitto, H. D. Navone, R. D. Piacentini & H. A. Ceccatto, Solar Physics 191, 419 (2000).
  • 2. A Learning Algorithm for Neural Networks Ensembles, H. D. Navone, P. M. Granitto, P. F. Verdes & H. A. Ceccatto, Revista Iberoamericana de Inteligencia Artificial 12, 70 (2001).
  • 3. A Late-Stopping Method for Optimal Aggregation of Neural Networks, P.M. Granitto, P.F. Verdes, H.D. Navone & H.A. Ceccatto, International Journal of Neural Systems 11, 305 (2001).
  • 4. Non-Stationary Time-Series Analysis: Accurate Reconstruction of Driving Forces, P. F. Verdes, P. M. Granitto, H. D. Navone & H. A. Ceccatto, Physical Review Letters 87, 124101 (2001).
  • 5. Weed Seeds Identification by Machine Vision, P. M. Granitto, H. D. Navone, P. F. Verdes & H. A. Ceccatto, Computers and Electronics in Agriculture 33, 91 (2002).
  • 6. Modeling Non-Stationary Dynamics, M.I. Szeliga, P. F. Verdes, P. M. Granitto & H. A. Ceccatto, Physica A, 327:1-2, 190 (2003).
  • 7. Artificial Neural Networks Learning of Non-Stationary Behavior in Time Series, M.I. Szeliga, P. F. Verdes, P. M. Granitto & H. A. Ceccatto, International Journal of Neural Systems, 13:2, 103 (2003).
  • 8. Secular behavior of Solar Dynamics: Non-Stationary Time-Series Analysis of the Sunspots Record, P. F. Verdes, P. M. Granitto & H. A. Ceccatto, Solar Physics, 221:1, 167 (2004).
  • 9. Large Scale Investigation of Weed Seeds Identification by Machine Vision Techniques, P. M. Granitto, P. F. Verdes & H. A. Ceccatto, Computers and Electronics in Agriculture, 47:1, 15 (2005).
  • 10. Neural Networks Ensembles: Evaluation of Aggregation algorithms, P. M. Granitto, P. F. Verdes & H. A. Ceccatto, Artificial Intelligence, 163:2, 139 (2005).
  • 11. Recursive feature elimination with Random Forest for PTR-MS analysis of agroindustrial products, P. M. Granitto, C. Furlanello, F. Biasioli & F. Gasperi, Chemometrics and Intelligent Laboratory Systems, 83:2, 83 (2006).
  • 12. Overembedding method for modeling nonstationary systems, P.F. Verdes, P.M. Granitto & H.A. Ceccatto, Physical Review Letters, 96, 118701 (2006).
  • 13. Modern data mining tools in descriptive sensory analysis: A case study with a Random forest approach, P. M. Granitto, F. Gasperi, F. Biasioli, E. Trainotti & C. Furlanello. Food Quality and Preferences., 18:4, 681 (2007).
  • 14. Coupling Proton Transfer Reaction-Mass Spectrometry with data mining techniques: classification of strawberry cultivars. P. M. Granitto, F. Biasioli, E. Aprea, D. Mott, C. Furlanello, T. D. Mark & F. Gasperi, Sensors and Actuators B., 121:2, 379 (2007).
  • 15. Prediction of the CATS benchmark exploiting time-reversal symmetry, P.F. Verdes, P.M. Granitto, M.I. Szeliga, A. Rebola & H.A. Ceccatto, Neurocomputing, 13-15, 2363 (2007).
  • 16. Prediction of minimum temperatures in an alpine region by linear and nonlinear post-processing of meteorological models, E. Eccel, L. Ghielmi, P.M. Granitto, R. Barbiero, F. Grazzini & D. Cesari, Nonlinear Processes in Geophysics, 14:3, 221 (2007).
  • 17. Discriminant models based on sensory evaluations: single assessors versus panel average, P.M. Granitto, F. Biasioli, I. Endrizzi & F. Gasperi, Food Quality and Preference, 19, 589 (2008).
  • 18. ISOMAP Based Metrics for Clustering, A.E. Bayá & P.M. Granitto, Revista Iberoamericana de Inteligencia Artificial, 37, 15 (2008).
  • 19. Time-Adaptive Support Vector Machines, G.L. Grinblat, P. M. Granitto & H.A.Ceccatto, Revista Iberoamericana de Inteligencia Artificial, 40, 39 (2008).
  • 20. Feature selection on wide multiclass problems using OVA-RFE, P. M. Granitto & A. Burgos, Revista Iberoamericana de Inteligencia Artificial, 44, 27 (2009).
  • 21. PTR-TOF-MS and data mining methods for rapid characterization of agroindustrial samples: influence of milk storage conditions on the volatile compounds profile of Trentingrana cheese. A. Fabris, F. Biasioli, P.M.. Granitto, E. Aprea. L. Cappellin, E. Schuhfried, C. Soukoulis, T.D. Maerk, F. Gasperi, I. Endrizzi, Journal of Mass Spectrometry, 45, 1065 (2010).
  • 22. Solving non-stationary classification problems with coupled support vector machines. G.L. Grinblat, L.C. Uzal, H.A. Ceccatto & P.M. Granitto. IEEE T. on Neural Networks, 22, 37 (2011).
  • 23. Clustering gene expression data with a penalized graph-based metric. A.E. Baya & P.M. Granitto. BMC Bioinformatics, 12, 2 (2011)
  • 24. On data analysis in PTR-TOF-MS: From raw spectra to data mining, Cappellin, L, Biasioli, F, Granitto, P.M., Schuhfried, E, Soukoulis, C., Costa, F., Märk, T.D., Gasperi, F. Sensors and Actuators B, 155, 183 (2011).
  • 25. Rapid characterization of dry cured ham produced following different PDOs by proton transfer reaction time of flight mass spectrometry (PTR-ToF-MS), J. Sanchez del Pulgar, C. Soukoulis, F. Biasioli, L. Cappellin, C. Garcia, F. Gasperi, P.M. Granitto, T. D. Mark, E. Piasentier, E. Schuhfried, Talanta, 85(1), 386, (2011).
  • 26. Evaluation of a new hybrid algorithm for highly imbalanced classification problems. H.C. Ahumada, G.L. Grinblat, L.C. Uzal, H.A. Ceccatto & P.M. Granitto. International Journal of Hybrid Intelligent System, 8(4), 199 (2011).
  • 27. PTR-ToF-MS and data mining methods: a new tool for fruit metabolomics, L. Cappellina, C. Soukoulis, E. Aprea, P.M. Granitto, N. Dallabetta, F. Costa, T.D. Maerk, F. Gasperi & F. Biasioli, Metabolomics, 8(5), 761 (2012).
  • 28. Linking GC-MS and PTR-TOF-MS fingerprints of food samples, L. Cappellin, E.Aprea, P. M. Granitto, R. Wehrens, C. Soukoulis, T. D. Märk, F. Gasperi & F. Biasioli, Chemometrics and Intelligent Laboratory Systems, 118, 301-310 (2012).
  • 29. Effect of the pig rearing system on the final volatile profile of Iberian dry-cured ham as detected by PTR-ToF-MS. J. Sánchez del Pulgar, C. Soukoulis, A.I. Carrapiso, L. Cappellin, P.M. Granitto, E. Aprea, A. Romano; F. Gasperi & F. Biasioli, Meet Science, 93 (3), 420-428 (2013).
  • 30. Spot defects detection in cDNA microarray images, M. Larese, P.M. Granitto & J.C. Gomez, Pattern Analysis and Application, 16, 307-319 (2013).
  • 31. Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS. L. Cappellin, E. Aprea, P.M. Granitto, A. Romano, F. Gasperi & F. Biasioli, Food Research International, 54, 1313-1320 (2013).
  • 32. How many Clusters: A Validation Index for arbitrary shaped clusters. A.E. Baya & P.M. Granitto. IEEE/ACM T. on Computational Biology and Bioinformatics, 10(2), 401-414 (2013).
  • 33. Abrupt Change Detection with One-Class Time-Adaptive Support Vector Machines. G.L. Grinblat, L.C. Uzal & P.M. Granitto. Expert Systems with Applications, 40(18), 7242-7249 (2013).
  • 34. Automatic classification of legumes using leaf vein image features. M. G. Laresea, R. Namías, R. M. Craviotto, M. R. Arango, C. Gallo & P. M. Granitto. Pattern Recognition, 47(1), 158-168 (2014).

Conferences:

  • 1. Selecting Diverse Members of a Neural Network Ensemble, H. D. Navone, P. F. Verdes, P. M. Granitto & H. A. Ceccatto, Proceedings of SBRN 2000-The VI Brazilian Symposium on Neural Networks, IEEE, Rio de Janeiro, Brazil, pages 255-260 (2000).
  • 2. Automated Detection and Classification of Clustered Microcalcifications Using Morphological Filtering and Statistical Techniques, G.H.Kaufmann, M.F. Salfity, P.M. Granitto & H.A. Ceccatto, Fifth International Workshop on Digital Mammography, Toronto, Canada (2000).
  • 3. Aggregation Algorithms for Neural Network Ensemble Construction, P.M. Granitto, P.F. Verdes, H.D. Navone & H.A. Ceccatto, Proceedings of SBRN 2002-The VII Brazilian Symposium on Neural Networks, IEEE, Recife, Brazil (2002).
  • 4. Extracting Driving Signals from Non-Stationarity Time Series, M. I. Szeliga, P.F. Verdes, P. M. Granitto & H.A. Ceccatto, Proceedings of SBRN 2002-The VII Brazilian Symposium on Neural Networks, IEEE, Recife, Brazil (2002).
  • 5. Prediction of the CATS benchmark exploiting time-reversal symmetry, P.F. Verdes, P.M. Granitto, M.I. Szeliga, A. Rebola & H.A. Ceccatto, IJCNN 04, International joint conference on Neural Networks 04, LNCS, Budapest, Hungary (2004).
  • 6. Tecniche di post-elaborazione di temperatura minima a confronto per un'area alpina, E. Eccel, R. Barbiero, D. Cesari, L. Ghielmi, P.M. Granitto & F. Grazzini, 9 convegno nazionale di agrometeorologia, Turin, Italia (2006). Publicado en Italian Journal of Agrometeorology, 11, 1 (sup), 53-54 (2006).
  • 7. Gene Set Enrichment Analysis Using Non-parametric Scores, A.E. Bayá, M.G. Larese, P.M. Granitto, J.C. Gómez & E. Tapia, Proceedings of the Brazilian Symposium on Bioinformatics, LNBI, Angra dos Reis, Brazil (2007)
  • 8. Efficient Feature Selection for PTR-MS Fingerprinting of Agroindustrial Products, P. M. Granitto, F. Biasioli, C. Furlanello & F. Gasperi, ICANN08, Proceedings of the 18th International Conference on Artificial Neural Network, LNCS, Prague, Czech Republic (2008).
  • 9. REPMAC: A new hybrid approach to higly imbalanced classification problems, H. Ahumada, G.L. Grinblat, L.C. Uzal, P. M. Granitto & H.A. Ceccatto, Proceedings of the Eighth International Conference on Hybrid Intelligent Systems HIS08, IEEE, Barcelona, Spain (2008).
  • 10. SVM based feature selection: why are we using the dual?, G.L. Grinblat, J. Izetta & P. M. Granitto, Proceedings of IBERAMIA 2010, LNCS, Bahia blanca, Argentina, (2010).
  • 11. Improved graph-based metrics for clustering high-dimensional datasets, A. Baya & P. M. Granitto, Proceedings of IBERAMIA 2010, LNCS, Bahia blanca, Argentina, (2010).
  • 12. Learning to discover faulty spots in cDNA microarrays, M. Larese, P. M. Granitto & J. C. Gomez, Proceedings of IBERAMIA 2010, LNCS, Bahia blanca, Argentina, (2010).
  • 13. Unsupervized Data-Driven Partitioning of Multiclass Problems, H.C. Ahumada, G.L. Grinblat & P.M. Granitto, ICANN11, Proceedings of the 21th International Conference on Artificial Neural Network, LNCS, Espoo, Finland (2011).
  • 14. Improved gene expression clustering with the parameter-free PKNNG metric, A.E. Bayá & P.M. Granitto, Proceedings of the Brazilian Symposium on Bioinformatics, LNBI, Brasilia, Brazil (2011).
  • 15. A Simple Hybrid Method for Semi-Supervised Learning. H.C. Ahumada & P. M. Granitto, Proceedings of the Iberoamerican Congress on Pattern Recognition, CIARP 17, LNCS, Buenos Aires, Argentina (2012).
  • 16. Legume identification by leaf vein images classification. M. Larese, C. Gallo, R. Craviotto, M. Arango and P. M. Granitto. Proceedings of the Iberoamerican Congress on Pattern Recognition, CIARP 17, LNCS, Buenos Aires, Argentina (2012).

Book Chapters:

  • 1. Forecasting Chaotic Time Series: Global vs. Local Methods, P. F. Verdes, P. M. Granitto, H. D. Navone & H. A. Ceccatto, Novel Intelligent Automation and Control Systems, Vol 1, Ed. J. Pfeiffer, 129-145, ALFA-NIACS (1998).
  • 2. Qualità sensoriale e specificità dei formaggi tipici, Gasperi F., Biasioli F., Framondino V., Endrizzi I., Granitto P.M. Caratterizzazione di formaggi tipici dell’arco alpino: Il contributo della ricerca, Ed. F. Gasperi & G. Versini, 261-272, Istituto Agrario di S. Michele all’Adige (2005).
  • 3. L'analisi della frazione volatile: fra riferimenti sicuri e nuove possibilità, F. Biasioli, A. Fabris, E. Aprea, P.M. Granitto, L. Cappellin, E. Schuhfried, C. Soukoulis, E. Betta, I. Endrizzi, T.D. Märk & F. Gasperi. La filiera del Grana trentino : approcci innovativi e integrati alla tecnologia e al controllo qualità . Ed. F. Gasperi & A. Cavazza. 107-116, Istituto Agrario di San Michele all’Adige (2012).

Other publications (National journals and conferences):

  • 1. Entrenamiento de Redes Neuronales: comparación de diferentes algoritmos de minimización, P. M. Granitto, P. F. Verdes, H. D. Navone & H. A. Ceccatto, Anales de la Asociación Física Argentina 9, 20 (1997).
  • 2. Reconstrucción de dinámicas caóticas con datos escasos, P. F. Verdes, P. M. Granitto, H. D. Navone & H. A. Ceccatto, Anales de la Asociación Física Argentina 10, 22 (1998).
  • 3. Comparación de métodos no lineales en la predicción de series temporales ruidosas, P. M. Granitto, P. F. Verdes, H. D. Navone & H. A. Ceccatto, Anales de la Asociación Física Argentina 11, 33 (1999).
  • 4. Predicción de temperaturas mínimas utilizando otras variables de superficie como predictores, P. F. Verdes, P. M. Granitto, H. D. Navone & H. A. Ceccatto, Anales de la Asociación Física Argentina 11, 351 (1999).
  • 5. Predicción del Ciclo Solar 23 con un algoritmo Adaline basado en funciones potenciales de base radial, H. D. Navone, P. F. Verdes, P. M. Granitto, & H. A. Ceccatto, Anales de la Asociación Física Argentina 12, 12 (2000).
  • 6. A new algorithm for selecting diverse members of a neural network ensemble, H. D. Navone, P. F. Verdes, P. M. Granitto & H. A. Ceccatto, Proceedings of ICIEY2K-The VI International Congress on Information Engineering, Universidad de Buenos Aires, Buenos Aires (2000).
  • 7. A learning algorithm for neural network ensembles, H. D. Navone, P. F. Verdes, P. M. Granitto & H. A. Ceccatto, Proceedings of JAIIO’2000-The 29th International Conference of the Argentine Computer Science and Operational Research Society, Tandil, Argentina, pages 199-207 (2000).
  • 8. A Computer-Aided Diagnosis Method for Automatic Detection and Classification of Clustered Microcalcifications in Mammograms, M.F. Salfity, G.H.Kaufmann, P.M. Granitto & H.A. Ceccatto, Proceedings of JAIIO’2000-The 29th International Conference of the Argentine Computer Science and Operational Research Society, Tandil, Argentina, pages 199-207 (2000).
  • 9. Frost Prediction with Machine Learning Techniques, P. F. Verdes, P. M. Granitto, H. D. Navone & H. A. Ceccatto, Proceedings of CACIC 2000-The VI Argentine Congress on Computer Science, Ushuaia, Argentina, pages 1423-1433 (2000).
  • 10. Automatic Identification of Weed Seeds by Color Image Processing, P. M. Granitto, P. F. Verdes, H. D. Navone & H. A. Ceccatto, Proceedings of CACIC 2000-The VI Argentine Congress on Computer Science, Ushuaia, Argentina, pages 229-236 (2000).
  • 11. Detección y clasificación automática de microcalcificaciones agrupadas en Mamografías, M.F. Salfity, G.H.Kaufmann, P.M. Granitto & H.A. Ceccatto, Informática Médica, 8: 5 (2001).
  • 12. Learning External Perturbations from Non-Stationary Signals, P. F. Verdes, P. M. Granitto, H. D. Navone & H. A. Ceccatto, Proceedings of JAIIO 2001-The 30th International Conference of the Argentine Computer Science and Operational Research Society, Buenos Aires, Argentina, pages 108-116 (2001).
  • 13. Modeling of Sonic Logs in Oil Wells with Neural Networks Ensembles, P. M. Granitto, P. F. Verdes, H. D. Navone, H. A. Ceccatto, D. Curia & C. Larriestra, Proceedings of JAIIO 2001-The 30th International Conference of the Argentine Computer Science and Operational Research Society, Buenos Aires, Argentina, pages 31-37 (2001).
  • 14. Modeling Sonic Logs in Oil Wells: A comparison of Neural Networks Ensembles and Kernel Methods, P. M. Granitto, H. D. Navone, P. F. Verdes & H. A. Ceccatto, Proceedings of CACIC 2001-The VII Argentine Congress on Computer Science, Calafate, Argentina (2001).
  • 15. SECA: A Stepwise Algorithm for Construction of Neural Networks Ensembles, P. M. Granitto, H. D. Navone, P. F. Verdes & H. A. Ceccatto, Proceedings of CACIC 2001-The VII Argentine Congress on Computer Science, Calafate, Argentina (2001).
  • 16. Boosting Classifiers for Weed Seeds Identification, P.M. Granitto, P. Garralda, P.F. Verdes & H.A. Ceccatto, Proceedings of CACIC 2002-The VIII Argentine Congress on Computer Science, Buenos Aires, Argentina (2002).
  • 17. Boosting Classifiers for Weed Seeds Identification, P.M. Granitto, P.A. Garralda, P.F. Verdes and H.A. Ceccatto, Journal of Computer Science and Technology, 3:1, 34 (2003).
  • 18. Aggregation Algorithms for Regression. A Comparison with Boosting and SVM Techniques, P.M. Granitto, P.F. Verdes & H.A. Ceccatto, Proceedings of CACIC 2003-The IX Argentine Congress on Computer Science, La Plata, Argentina (2003).
  • 19. Automatic Identification of Weed Seeds, P.M. Granitto, P.F. Verdes & H.A. Ceccatto, Proceedings of JAIIO 2003-The 32th International Conference of the Argentine Computer Science and Operational Research Society, Buenos Aires, Argentina (2003).
  • 20. Modeling Sensory Analysis datasets: the case of Italian Cheeses. , P. M. Granitto, F. Gasperi, F. Biasioli & C. Furlanello, Proceedings of JAIIO 2005-The 34th International Conference of the Argentine Computer Science and Operational Research Society, Rosario, Argentina (2005).
  • 21. Cascade Classifiers for multiclass problems, P. M. Granitto, A. Rebola, U. Cervino, F. Gasperi, F. Biasioli & H.A. Ceccatto, Proceedings of JAIIO 2005-The 34th International Conference of the Argentine Computer Science and Operational Research Society, Rosario, Argentina (2005).
  • 22. ISOMAP Based Metrics for Clustering, A.E. Bayá & P.M. Granitto, Proceedings of JAIIO 2007-The 36th International Conference of the Argentine Computer Science and Operational Research Society, Mar del Plata, Argentina (2007)
  • 23. Random Forest-like strategies for Neural Network Ensembles Construction, R. Namías & P.M. Granitto, Proceedings of CACIC 2007-The XIII Argentine Congress on Computer Science, Corrientes, Argentina (2007).
  • 24. Coupling REPMAC with FDA to solve higly imbalanced classification problems, H. Ahumada, G.L. Grinblat, L.C. Uzal, H.A. Ceccatto & P.M. Granitto, Proceedings of CACIC 2008- XIV Argentine Congress on Computer Science, Chilecito, Argentina, (2008).
  • 25. Clustering Gene Expression data with the PKNNG metric, A. E. Bayá & P. M. Granitto, Proceedings of CACIC 2008- XIV Argentine Congress on Computer Science, Chilecito, Argentina, (2008).
  • 26. Feature selection on wide multiclass problems using OVA-RFE. P.M. Granitto & A. Burgos, Proceedings of JAIIO 2009-The 38th International Conference of the Argentine Computer Science and Operational Research Society, Mar del Plata, Argentina (2009).
  • 27. Gaining knowledge of data structure using stability concepts. A.E. Bayá & P.M. Granitto, Proceedings of JAIIO 2009-The 38th International Conference of the Argentine Computer Science and Operational Research Society, Mar del Plata, Argentina (2009).
  • 28. Feature selection with simple ANN ensembles, J. Izetta & P. M. Granitto, Proceedings of CACIC 2009- XV Argentine Congress on Computer Science, Jujuy, Argentina, (2009).
  • 29. Extended evaluation of the UPM method for multiclass problems, H.C. Ahumada, G.L. Grinblat & P.M. Granitto, Proceedings of JAIIO 2011-The 40th International Conference of the Argentine Computer Science and Operational Research Society, Cordoba, Argentina (2011).
  • 30. New strategies for OVO Feature Selection on Multiclass Problems, J.C. Izetta, G.L. Grinblat & P.M. Granitto, Proceedings of JAIIO 2011-The 40th International Conference of the Argentine Computer Science and Operational Research Society, Cordoba, Argentina (2011).
  • 31. Automatic Grading of Green Intensity in Soybean Seeds, R. Namias, C. Gallo, R. M. Craviotto, M. R. Arango & P.M. Granitto, Proceedings of JAIIO 2012-The 41st International Conference of the Argentine Computer Science and Operational Research Society, La Plata, Argentina (2012).
  • 32. Deep Architectures on Drifting Concepts: A Simple Approach, L. Morelli, P.M. Granitto, G.L. Grinblat. Proceedings of JAIIO 2013-The 42nd International Conference of the Argentine Computer Science and Operational Research Society, Cordoba, Argentina (2013).