RESEARCH INTERESTS AND PUBLICATIONS


 

Research interests:

She mainly works in the fields of neural networks, machine learning and data mining methods to perform classification, diagnosis and regression tasks.
Since 2000 she collaborates with the Astronomical Observatory of Torino, working on astronomical data analysis and diagnosis tasks.

Visiting researcher since 2010 at Laboratoire d’Informatique de Paris 6 (LIP6), Pierre and Marie Curie University - Paris - France.

Journals:

 

 F. van Leeuwen et al. Gaia Data Release 1. Open cluster astrometry: performance, limitations, and future prospects, Astronomy & Astrophysics 601, A19 (2017), DOI: 10.1051/0004-6361/201730552

 R. Cancelliere,  R. Deluca, M. Gai, P. Gallinari, L. Rubini.  An analysis of numerical issues in neural training based on pseudoinversion, Computational and Applied Mathematics, 36(1), (2017), pp.599-609, DOI: 10.1007/s40314-015-0246-z.

 M. Gai, D. Busonero, R. Cancelliere. Performance of an Algorithm for Estimation of Flux, Background, and Location on One-dimensional Signals, Pasp, Vol. 129, (2017).

 L. Rubini, R. Cancelliere,  P. Gallinari, A. Grosso.  Local Search and Pseudoinversion: an Hybrid Approach to Neural Network Training, Knowledge and Information Systems (KAIS), (2016), DOI: 10.1007/s10115-016-0935-y.

 L. Ghignone, R. Cancelliere.  Neural Learning of Heuristic Functions for General Game Playing, Lecture Notes in Computer Science, vol. 10122, Springer-Verlag, (2016).

 T. Prusti et al. The Gaia mission, Astronomy & Astrophysics 595, A1 (2016), DOI: 10.1051/0004-6361/201629272

 A.G.A. Brown et al. Gaia Data Release 1. Summary of the astrometric, photometric, and survey properties, Astronomy & Astrophysics 595, A2 (2016), DOI:10.1051/0004-6361/201629512

 C. Fabricius et al. Gaia Data Release 1. Pre-processing and source list creation, Astronomy & Astrophysics 595, A3 (2016), DOI: 10.1051/0004-6361/201628643

 R. Cancelliere,  M. Gai, P. Gallinari, L. Rubini.  OCReP: An Optimally Conditioned Regularization for pseudoinversion based neural training, Neural Networks, vol. 71, (2015), pp.76-87, DOI: http://dx.doi.org/10.1016/j.neunet.2015.07.015.

 L. Rubini, R. Cancelliere,  P. Gallinari, A. Grosso, A. Raiti.  Computational Experience with Pseudoinversion-Based Training of Neural Networks Using Random Projection Matrices, Lecture Notes in Computer Science, vol. 8722, (2014), pp.236-245.

 M. Gai, D. Busonero, R. Cancelliere. Statistically optimal fitting of astrometric signals, Pasp, Vol. 125, No. 926 (2013), pp. 444-455.

 R. Cancelliere,  M. Gai, T. Artières, P. Gallinari.  Matrix pseudoinversion for image neural processing, Lecture Notes in Computer Science, vol. 7667, part V, Springer-Verlag Berlin Heidelberg, (2012), pp.116-125.

 R. Cancelliere, M. Gai, A. Slavova,  Application of Polynomial Cellular Neural Networks in Diagnosis of Astrometric Chromaticity, Applied Mathematical Modelling, vol. 34, n. 12, (2010), pp. 4243-4252,  Download

 M. Gai, R. Cancelliere, D. Busonero, Astrometric signal profile fitting for Gaia, Mnras, vol. 406, (2010), pp. 2433-2444.

 R. Cancelliere, M. Gai, Efficient computation and Neural Processing of Astrometric Images, Computing and Informatics, vol. 28, (2009), pp. 711-727, Download

 M. Gai, R. Cancelliere, Astrometric effects of non-uniform telescope throughput, Mnras, vol. 391, (2008), pp. 1451-1456.

 M. Gai, R. Cancelliere, An efficient point spread function construction method, Mnras, vol. 377, (2007), pp. 1337-1342.

 R. Cancelliere, A. Slavova, Dynamics and Stability of Generalized Cellular Nonlinear Network Model, Applied Mathematics and Computations, vol. 165 (1), (2005),

     pp. 127-136,  Download   

 M. Gai, R. Cancelliere, Neural network correction of astrometric chromaticity, Mnras, vol. 362 (4), (2005), pp. 1483-1488,  Download

 R. Cancelliere, M. Gai, Function Approximation of Seidel Aberrations by a Neural Network, Bollettino Unione Matematica, vol. 8, n.7-B, (2004), pp. 687-696.

 R. Cancelliere, Data Processing and Feature Screening in Function Approximation: an Application to Neural Networks, Computers and Mathematics with Applications, vol. 46, (2003), pp. 455–461,   Download

 R. Cancelliere, M. Gai, A Comparative Analysis of Neural Network Performances in Astronomy Imaging, Applied Numerical Mathematics, vol. 45, n.1, (2003), pp.87-98.

 R. Cancelliere, M. Gai, On the approximation error introduced using Principal Component Analysis in Neural Networks, Nonlin. Anal., vol. 47, (2001), pp. 5785-5794.

 R. Cancelliere, A High Parallel Procedure to initialize the Output Weights of a Radial Basis Function or BP Neural Network, Lecture Notes in Computer Science, Springer Verlag, vol. 1947, (2000), pp. 385-391.

 R. Cancelliere, R. Gemello, Efficient training of time delay neural networks for sequential patterns, Neurocomputing, vol. 10, (1996), pp. 33-42.

 M. Anselmino, R. Cancelliere, F. Murgia, Mass corrections to ``forbidden'' charmonium decays: ,  -> , Phys. Rev.D 46 (1992), pp. 5049.

 

Conferences:

 L. Ghignone, R. Cancelliere.  Neural Learning of Heuristic Functions for General Game Playing, 2rd International Workshop on Machine learning, Optimization & big Data - MOD 2016, August 26-29, 2016 - Volterra (Pisa), Italy.

 L. Rubini, R. Cancelliere,  P. Gallinari, A. Grosso, A. Raiti.  Computational Experience with Pseudoinversion-Based Training of Neural Networks Using Random Projection Matrices, Proc. 16th International Conference on Artificial Intelligence: Methodology, Systems, Applications, AIMSA 2014, G. Agre et al. (Eds.), Springer International Publishing Switzerland, (2014), pp.236-245.

 R. Cancelliere, A. Gosso, A. Grosso. Neural Networks for Wind Power Generation Forecasting: a Case Study, Proceedings of the 10th IEEE International Conference on Networking, Sensing and Control ICNSC 2013, Paris, France, 10-12/4/2013.

 R. Cancelliere,  M. Gai, T. Artières, P. Gallinari.  Matrix pseudoinversion for image neural processing, Proceedings of the 19th International Conference on Neural Information Processing ICONIP 2012, T. Huang et al. (Eds.), Doha, Qatar, 11-15/11/2012, Download

  E.Roglia, R.Cancelliere, R.Meo. Classification of Chestnuts with Feature Selection by Noise Resilient Classifiers, Proceedings of the 16th European Symposium on Artificial Neural Networks, ESANN'2008, Bruges (Belgium), 23-25/4/ 2008.

  R.Cancelliere, M. Gai. Neural Network High Precision Processing for Astronomical Images, Proceedings of the 15th European Signal Processing Conference EUSIPCO 2007, Poznan, (Poland), 3-7/9/2007.

  R.Cancelliere, M. Gai. Input features selection for neural data analysis in astronomical imaging, Proceedings of the 16th IASTED International Conference on Applied Simulation and Modelling, Palma de Maiorca, (Spain), 29-31/8/2007.

  C. Beltramo, M. Botta, R. Cancelliere, C. Sartor, P. Guaraldo, R. Botta. Valorizzazione della qualità e tracciabilità delle produzioni melicole piemontesi mediante un approccio multidisciplinare, Riassunto dei lavori VIII giornate scientifiche SOI Italus Hortus, Sassari, 8-11/5/2007.

  R. Cancelliere. The Sensitivity Matrix of a Neural Network Performing Image Processing, Proceedings of the 17th IMACS World Congress, Paris, France, 11-15/7/2005.

  R. Cancelliere e M. Gai. Neural Network Performances in Astronomical Image Processing, Proceedings of the 11th European Symposium on Artificial Neural Networks, ESANN'2003, Bruges (Belgium), 23-25/4/2003.

  R. Cancelliere, M. Gai. A performance comparative analysis of some kind of Neural Networks, Proceeding of the 5th IMACS Conference on Iterative Methods in Scientific Computing, Heraklion, Crete, Greece, 28-31/5/2001.

  R. Cancelliere, M. Gai. On the approximation error introduced using Principal Component Analysis in Neural Networks, Proceedings of the 3thWorld Congress of Nonlinear Analysts, Catania, Italy, 19-26/7/2000.

  R. Cancelliere. Speeding up MLP execution by approximating neural networks activation functions, Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Cambridge, England, 31/8 - 3/9/1998.

  R. Cancelliere. Large database recognition tasks: a proposal for partitioning the data matrix required to train a Radial Basis Functions Network, Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing, Venezia, 21-23/8/1996.

  R.Gemello, D.Albesano, F.Mana, R.Cancelliere. Recurrent network automata for speech recognition: a summary of recent work, Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Ermioni, Greece, 6-8/9/1994. 

 

Chapters in book:

 P. Pisano, R. Cancelliere. Modelli economici previsionali basati su clustering e reti neurali, in:  ECONOMIA E GESTIONE DELLE IMPRESE E DEI SISTEMI COMPETITIVI, Alcuni percorsi di ricerca interdisciplinare nell’ambito delle scienze manageriali, M. Pironti (Eds.), (2012).

 

Technical Reports:

R. Cancelliere, R. De Luca, M. Gai, P. Gallinari, T. Artières.  Pseudoinversion for neural training: tuning the regularisation parameter, Rapporto Tecnico RT149/13, Dipartimento di Informatica, Università di Torino, (2013).

A. Antonini, R. Cancelliere,  T. Artières, P. Gallinari.  Deep neural networks training: perspectives of investigation (in italian), Rapporto Tecnico RT145/12, Dipartimento di Informatica, Università di Torino, (2012).

T. Artières, R. Cancelliere,  M. M. Cisse, M. Gai,  P. Gallinari Random projections and matrix pseudoinversion for neural training, Rapporto Tecnico RT132/10, Dipartimento di Informatica, Università di Torino, (2010).

R. Cancelliere, M. Gai, E. Roglia, M. Botta. Comparison of computational efficiencies of ANN algorithms for regression tasks, Rapporto Tecnico RT119/09, Dipartimento di Informatica, Università di Torino, (2009).

R. Cancelliere,  E. Roglia, M. Visconti di Oleggio Castello. Data classification with feature selection by noise resilient classifiers, Rapporto Tecnico RT122/09, Dipartimento di Informatica, Università di Torino, (2009).

E.Roglia, R.Cancelliere, R.Meo. Classification of Chestnuts with Experiments on Feature Selection and Noise, Rapporto Tecnico RT100/07, Dipartimento di Informatica, Università di Torino, (2007).

M. Gai, D. Busonero e R. Cancelliere. Preliminary assessment of an efficien PSF/LSF model for Gaia, Gaia Livelink pages, (2006), http://www.rssd.esa.int/

 




Last updated version: June, 18th, 2004