Publications

[77] F. Cavalli, L. Lusnig, and E. Trentin. Use of Pattern Recognition and Neural Networks for Non-Metric Sex Diagnosis From Lateral Shape of Calvarium: An Innovative Model for Computer-Aided Diagnosis in Forensic and Physical Anthropology. International Journal of Legal Medicine: 1-11 (2016) doi:10.1007/s00414-016-1439-8.

[76] E. Trentin. Soft-Constrained Nonparametric Density Estimation with Artificial Neural Networks. In Proc. of ANNPR 2016: 68-79, Springer (2016).

[75] M. Bongini, V. Laveglia, and E. Trentin. A Hybrid Recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins. In Proc. of ANNPR 2016: 257-268, Springer, 2016.

[74] F. Schwenker, H. M. Abbas, N. El Gayar, and E. Trentin (Eds.). Artificial Neural Networks in Pattern Recognition - 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016, Proceedings. Lecture Notes in Computer Science 9896, Springer (2016).

[73] E. Trentin and M. Bongini. Probabilistically Grounded Unsupervised Training of Neural Networks. In M.E. Celebi and K. Aydin (Eds.) Unsupervised Learning Algorithms: 533-558 Springer (2016).

[72] E. Trentin. Maximum-likelihood normalization of features increases the robustness of neural-based spoken human-computer interaction. Pattern Recognition Letters 66(C): 71--80 (2015).

[71] E. Trentin, S. Scherer, F. Schwenker. Emotion recognition from speech signals via a probabilistic echo-state network. Pattern Recognition Letters 66(C): 4-12 (2015).

[70] M. Aste, M. Boninsegna, A. Freno, E. Trentin. Techniques for dealing with incomplete data: a tutorial and survey. Pattern Analysis and Applications 18(1): 1-29 (2015).

[69] M. Bongini, F. Schwenker, E. Trentin. On Semi-Supervised Clustering. In Celebi, M.E. (Ed.) Partitional Clustering Algorithms: 277-311 Springer (2015).

[68] E. Trentin. Libero arbitrio e macchine intelligenti. In Tugnoli, C. (Ed.) Libero arbitrio - Teorie e prassi della libertÓ: Liguori, 2014. ISBN: 978-88-207-5330-6. In Italian.

[67] F. Schwenker and E. Trentin. Pattern Classification and Clustering: a Review of Partially Supervised Learning Approaches. Pattern Recognition Letters 37: 4-14 (2014).

[66] F. Schwenker and E. Trentin. Partially Supervised Learning for Pattern Recognition. Pattern Recognition Letters 37: 1-3 (2014).

[65] I. Castelli and E. Trentin. Combination of supervised and unsupervised learning for training the activation functions of neural networks. Pattern Recognition Letters 37: 178-191 (2014).

[64] M. Glodek, E. Trentin, F. Schwenker, and G. Palm. Hidden Markov Models With Graph Densities for Action Recognition. In Proc. of IJCNN 2013 (INNS IEEE International Joint Conference on Neural Networks), Dallas (2013).

[63] M. Bongini and E. Trentin. Towards a Novel Probabilistic Graphical Model of Sequential Data: A Solution to the Problem of Structure Learning and an Empirical Evaluation. In Proc. of ANNPR 2012: 82-92 (2012).

[62] E. Trentin and M. Bongini. Towards a Novel Probabilistic Graphical Model of Sequential Data: Fundamental Notions and a Solution to the Problem of Parameter Learning. In Proc. of ANNPR 2012: 72-81 (2012).

[61] N. Mana, F. Schwenker, and E. Trentin (Eds.). Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Lecture Notes in Computer Science 7477, Springer 2012, ISBN 978-3-642-33211-1.

[60] F. Schwenker and E. Trentin (Eds.). Partially Supervised Learning - First IAPR TC3 Workshop, PSL 2011, Ulm, Germany, September 15-16, 2011, Revised Selected Papers.. Lecture Notes in Computer Science 7081, Springer 2012, ISBN 978-3-642-28257-7.

[59] A. Freno and E. Trentin. Hybrid Random Fields. A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models. Springer, ISBN 978-3-642-20307-7, 2011.

[58] I. Castelli and E. Trentin. Semi-Unsupervised Weighted Maximum-Likelihood Estimation of Joint Densities for the Co-Training of Adaptive Activation Functions. In Proceedings of the 1st IAPR-TC3 Workshop on Partially Supervised Learning (PSL 2011), Ulm (Germany), 2011.

[57] I. Castelli and E. Trentin. Supervised and Unsupervised Co-Training of Adaptive Activation Functions in Neural Nets. In Proceedings of the 1st IAPR-TC3 Workshop on Partially Supervised Learning (PSL 2011), Ulm (Germany), 2011.

[56] E. Trentin, L. Lusnig and F. Cavalli. Comparison of Combined Probabilistic Connectionist Models in a Forensic Application. In Proceedings of the 1st IAPR-TC3 Workshop on Partially Supervised Learning (PSL 2011), Ulm (Germany), 2011 (in press).

[55] A. Freno, E. Trentin and M. Gori. Kernel-Based Hybrid Random Fields for Nonparametric Density Estimation. In Proceedings of ECAI 2010, pages 427-432.

[54] E. Trentin, S. Scherer and F. Schwenker. Maximum Echo-State-Likelihood Networks for Emotion Recognition. In Proceedings of ANNPR 2010 (Artificial Neural Networks in Pattern Recognition, Fourth IAPR Workshop), pages 60-71, Cairo (Egypt), April 2010.

[53] E. Trentin, S. Zhang and M. Hagenbuchner. Recognition of Sequences of Graphical Patterns. In Proceedings of ANNPR 2010 (Artificial Neural Networks in Pattern Recognition, Fourth IAPR Workshop), pages 48-59, Cairo (Egypt), April 2010.

[52] E. Trentin and A. Freno. Probabilistic interpretation of neural networks for statistical and sequential pattern recognition. In Innovations in Neural Information Paradigms and Applications, Springer, SCI 247-0155, pages 155-182 (2009).

[51] E. Trentin and E. Di Iorio. Classification of graphical data made easy. Neurocomputing 73 (1-3): 204-212 (2009).

[50] S. Scherer, E. Trentin, F. Schwenker, and G. Palm. Approaching emotion in human computer interaction. In Proceedings of the International Workshop on Spoken Dialogue Systems, (satellite event of the IEEE ASRU 2009), Kloster Irsee (Germany), Dec. 2009.

[49] A. Freno, E. Trentin, and M. Gori. A hybrid random field model for scalable statistical learning. Neural Networks 22(5-6): 603-613 (2009).

[48] E. Trentin and L. Rigutini. A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains. In Proceedings of ICANN 2009, (1) 40-49.

[47] A. Freno, E. Trentin, and M. Gori. Scalable Pseudo-Likelihood Estimation in Hybrid Random Fields. In J.F. Elder, F. Fogelman-Soulie', P. Flach, and M. Zaki (eds.), Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2009). ACM, 2009, 319--327.

[46] E. Trentin and A. Freno. Unsupervised Nonparametric Density Estimation: A Neural Network Approach. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009). Atlanta, 2009, 3140--3147.

[45] A. Freno, E. Trentin, and M. Gori. Scalable Statistical Learning: A Modular Bayesian/Markov Network Approach. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009). Atlanta, 2009, 890--897.

[44] E. Trentin and E. Di Iorio. Classification of molecular structures made easy. Proceedings of IJCNN 2008, Hong Kong, June 2008, 3241-3246.

[43] E. Trentin and E. Di Iorio. Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition. In Proceedings of ICANN 2007, Porto, September 2007, (2) 271-280.

[42] E. Trentin and E. Di Iorio. A Simple and Effective Neural Model for the Classification of Structured Patterns. In Proceedings of KES 2007, Vietri Sul Mare (Italy), September 2007, (1) 9-16.

[41] V. Montagnani and E. Trentin. Hidden Markov Models and Multiple Transfer Functions for Voice Subtraction in an Acoustic Dosimeter. Circuits, Systems & Signal Processing, 26(3):311-323, June 2007.

[40] P. Fiengo, G. Giambene and E. Trentin. Neural-based downlink scheduling algorithm for broadband wireless networks. Computer Communications, 30(2):207--218, January 2007.

[39] E. Trentin and M. Gori. Inversion-based nonlinear adaptation of noisy acoustic parameters for a neural/HMM speech recognizer. Neurocomputing, 70(1-3):398--408, 2006.

[38] E. Trentin. A Novel Connectionist-Oriented Feature Normalization Technique. In Proceedings of ICANN 2006 (International Conference on Artificial Neural Networks), 410-416, Athena, Greece, September 2006.

[37] E. Trentin. Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions. In Proceedings of ANNPR 2006 (Artificial Neural Networks in Pattern Recognition, Second IAPR Workshop): 1-10, Ulm, Germany, August 31-September 2, 2006.

[36] E. Trentin and M. Gori. Feature normalization via ANN/HMM inversion for speech recognition under noisy conditions. In Proceedings of MMSP 2005, IEEE International Workshop on Multimedia Signal Processing, Shanghai, China, Oct-Nov 2005.

[35] E. Trentin and M. Gori. Robust Combination of Neural Networks and Hidden Markov Models for Speech Recognition. IEEE Transactions on Neural Networks, 14(6):1519--1531, November 2003.

[34] E. Trentin and M. Matassoni. Noise-tolerant speech recognition: the SNN-TA approach. Information Sciences (Special Issue on Intelligent Systems for Speech and Language), 156(1-2):55--69, November 2003.

[33] E. Trentin. Nonparametric Hidden Markov Models: Principles and Applications to Speech Recognition. In B. Apolloni, M. Marinaro and R. Tagliaferri (Editor), Neural Nets - WIRN Vietri - 2003, 3--24. Berlin, 2003, Springer-Verlag.

[32] E. Trentin, M. Matassoni and M. Gori. Evaluation on the Aurora 2 Database of Acoustic Models that are less Noise-sensitive. In Proceedings of Eurospeech 2003, Geneva, Switzerland, September 2003.

[31] E. Trentin, L. Magnoni and A. Andronico. Toward A Modular Connectionist Model of Local Chlorophyll Concentration from Satellite Images. In Proceedings of IJCNN03, IEEE-INNS International Joint Conference on Neural Networks, Portland (Oregon), July 2003.

[30] E. Trentin, F. Brugnara, Y. Bengio, C. Furlanello and R. De Mori. Statistical and neural network models for speech recognition. In R. Daniloff (Ed.) Connectionist accounts of clinical and normal language, 213--264, Mahwah, New Jersey, 2002. Lawrence Erlbaum Associates.

[29] E. Trentin and M. Gori. Toward Noise-tolerant Acoustic Models. In Proceedings of Eurospeech2001- Scandinavia, Aalborg, Denmark, September 2001.

[28] E. Trentin and M. Gori. Continuous Speech Recognition with a Robust Connectionist/Markovian Hybrid Model. In Proceedings of ICANN2001, Vienna, Austria, August 2001. Winner of the Best Paper Award.

[27] E. Trentin and D. Giuliani. A mixture of recurrent neural networks for speaker normalization. Neural Computing & Applications, July 2001, 10:120-135.

[26] E. Trentin. Networks with trainable amplitude of activation functions. Neural Networks 14 (July 2001), 471-493.

[25] E. Trentin and M. Gori. A survey of hybrid ANN/HMM models for automatic speech recognition. Neurocomputing, 37(1/4); 91-126, March 2001.

[24] E. Trentin. Robust Combination of Neural Networks and Hidden Markov Models for Speech Recognition. PhD Thesis, University of Florence (Italy), February 2001.

[23] E. Trentin and M. Matassoni. The regularized SNN-TA model for recognition of noisy speech. In Proceedings of IJCNN 2000 (International Joint Conference on Neural Networks), Como (Italy), 24-27 July 2000.

[22] E. Trentin and R. Cattoni. Learning perception for indoor robot navigation with a hybrid HMM/Recurrent neural networks approach. Connection Science, 11(3/4): 243-265, December 1999. Special Issue on Adaptive Robots.

[21] M. Matassoni, M. Omologo, L. Cristoforetti, D. Giuliani, P. Svaizer, E. Trentin, and E. Zovato. Some results on the development of a hands-free speech recognizer for car-environment. In Proceedings of the 1999 international workshop on Automatic Speech Recognition and Understanding (ASRU), Keystone, Colorado, USA, December 12-15 1999.

[20] E. Trentin. Activation functions with learnable amplitude. In Proceedings of IJCNN99, International Joint Conference on Neural Networks, Washington, DC, July 1999.

[19] E. Trentin and M. Gori. A tutorial on connectionist and hybrid HMM/connectionist systems for speech recognition. In N. Kasabov, editor, IJCNN99 Tutorials Track 8 (Book on CD-ROM, Chapter 3): Speech and Language Processing, Dunedin, New Zealand, 1999. University of Otago.

[18] E. Trentin and M. Matassoni. Robust segmental-connectionist learning for recognition of noisy speech. In Proceedings of the first Workshop on Robust Methods for Speech Recognition in Adverse Conditions, pages 159 - 162, Tampere, Finland, May 25-26 1999.

[17] C. Furlanello, D. Giuliani, S. Merler and E. Trentin. Model selection of combined neural nets for speech recognition. In A. J. C. Sharkey, editor, Combining Artificial Neural Nets - Ensemble and Modular Multi-Net Systems, pages 205 - 233, London, UK, 1999. Springer-Verlag.

[16] E. Trentin. HMMs for acoustic modeling: Beyond the problem of local stationarity. In Proceedings of IX AIA Conference on Computational Aspects of Phonetics, volume XXVI, Venezia, 1999.

[15] E. Trentin and M. Gori. Combining neural networks and hidden Markov models for speech recognition. In M. Marinaro and R. Tagliaferri, editors, Neural Nets - WIRN Vietri - 98, pages 63 - 79, Berlin, Germany, 1999. Springer-Verlag.

[14] E. Trentin. Learning the amplitude of activation functions in layered networks. In M. Marinaro and R. Tagliaferri, editors, Neural Nets - WIRN Vietri - 98, pages 138 - 144, Berlin, Germany, 1999. Springer-Verlag.

[13] E. Trentin and R. Cattoni. A hybrid framework for indoor robot navigation. In M. Marinaro and R. Tagliaferri, editors, Neural Nets - WIRN Vietri - 98, pages 255 - 263, Berlin, Germany, 1999. Springer-Verlag.

[12] E. Trentin, Y. Bengio, C. Furlanello and R. De Mori. Neural networks for speech recognition. In R. De Mori, editor, Spoken Dialogues with Computers, pages 311-361, London, UK, 1998. Academic Press.

[11] M. Boninsegna, T. Coianiz and E. Trentin. Estimating the crowding level with a neuro-fuzzy classifier. Journal of Electronic Imaging, 6(3):319-328, July 1997.

[10] E. Trentin and D. Giuliani. Speaker normalization with a mixture of recurrent networks. In Proceedings of ESANN97, European Symposium on Artificial Neural Networks, Bruges, Belgium, April 1997.

[9] E. Trentin and R. Cattoni. A hybrid HMM/recurrent neural networks approach to indoor robot navigation. In Proceedings of RWC97 - Real World Computing Symposium, Tokyo, Japan, January 1997.

[8] C. Furlanello, D. Giuliani, E. Trentin and S. Merler. Speaker normalization and model selection of combined neural nets. Connection Science, 9(1):31-50, January 1997. Special Issue on Combining Neural Nets.

[7] E. Trentin, D. Giuliani and C. Furlanello. Spectral mapping: A comparison of connectionist approaches. In M. Marinaro and R. Tagliaferri, editors, Neural Nets. WIRN Vietri-96, pages 270-277, Berlin, May 1996. Springer - Verlag.

[6] E. Trentin, C. Furlanello and D. Falavigna. An evaluation criterion for connectionist systems in classification tasks. In Proceedings of First Workshop on Evaluation criteria of neural nets efficiency in industrial applications, IIASS, Vietri sul Mare (SA), December 1995.

[5] C. Furlanello, D. Giuliani, E. Trentin and D. Falavigna. Applications of generalized radial basis functions in speaker normalization and identification. In Proceedings of ISCAS '95, IEEE International Symposium on Circuit and Systems, pages 867-874, Seattle WA, April, 30 - May, 3 1995.

[4] C. Furlanello, D. Giuliani and E. Trentin. Connectionist speaker normalization with Generalized Resource Allocating Networks. In G. Tesauro, D. S. Touretzky and T. K. Leen, editors, Advances in Neural Information Processing Systems 7, pages 1704-1707, Cambridge MA, 1995. MIT Press.

[3] E. Trentin and P. Miotti. HE: una macchina astratta per la gestione di sistemi ipermediali. Note di Software, 51:35-43, March 1991. In Italian.

[2] E. Trentin. I frattali - parte II. Bit, 107:126-131, July - August 1989. In Italian.

[1] E. Trentin. I frattali - parte I. Bit, 106:97-101, June 1989. In Italian.


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