Publications - Alessio Micheli

• my List of Publications on the University of Pisa web-site


• my Goolge Scholar page


• my Scopus page

Seven recent highlights (top conferences/awards)

• D. Tortorella, A. Micheli. "Minimum Spanning Set Selection in Graph Kernels".
  Best paper award at GbRPR 2023: International Workshop on Graph-Based Representations in Pattern Recognition. LNCS 14121, 2023.
  


• D. Castellana, F. Errica, D. Bacciu, A. Micheli. "The Infinite Contextual Graph Markov Model".
  ICML 2022: Proceedings of the 39th International Conference on Machine Learning. PMLR 162:2721-2737, 2022.
  


• D. Bacciu, F. Errica, A. Micheli. "Graph Mixture Density Networks" .
  ICML 2021: Proceedings of the 38th International Conference on Machine Learning. PMLR 139:3025-3035, 2021.
  


• F. Errica, M. Podda, D. Bacciu, A. Micheli. "A Fair Comparison of Graph Neural Networks for Graph Classification" .
  ICLR 2020 International Conference on Learning Representations


• C. Gallicchio, A. Micheli. "Fast and Deep Graph Neural Networks" .
  The 34-th AAAI conference on Artificial Intelligence, Febrary 2020, NY, USA


• P. Bove, A. Micheli, P. Milazzo, M. Podda. "Prediction of dynamical properties of biochemical pathways with graph neural networks" .
  Best paper award at the 11th International Conference on Bioinformatics Models, Methods and Algorithms @BIOSTEC 2020


• D. Bacciu, F. Errica, A. Micheli. "Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing" .
  ICML 2018: Proceedings of the 35th International Conference on Machine Learning. Vol. 80. pp. 294-303
  

Journal Papers (selected )

• M. Fontanesi, A. Micheli, P. Milazzo, M. Podda
  Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs.
  Bioinformatics, 2023, Vol. 39(11), btad678, ISSN 1367-4803, DOI: 10.1093/bioinformatics/btad678


• A. Micheli, D. Tortorella
  Addressing heterophily in node classification with graph echo state networks.
  Neurocomputing, 2023, Vol. 550, ISSN 0925-2312, DOI: 10.1016/j.neucom.2023.126506


• F. Errica, D. Bacciu, A. Micheli
  PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs.
  Journal of Open Source Software, 2023, Vol. 8(90), 5713, ISSN 2475-9066, DOI: 10.21105/joss.05713


• A. Micheli, D. Tortorella
  Discrete-time dynamic graph echo state networks.
  Neurocomputing, July 2022, Vol. 496, Pages 85-95, ISSN 0925-2312, DOI: 10.1016/j.neucom.2022.05.001


• L. Oneto, N. Navarin, B. Biggio, F. Errica, A. Micheli, F. Scarselli, M. Bianchini, L. Demetrio, P. Bongini, A. Tacchella, A. Sperduti
  Towards learning trustworthily, automatically, and with guarantees on graphs: An overview.
  Neurocomputing, July 2022, Vol. 493, Pages 217-243, ISSN 0925-2312, DOI: 10.1016/j.neucom.2022.04.072.


• F.M. Bianchi, C. Gallicchio. A. Micheli
  Pyramidal Reservoir Graph Neural Network.
  Neurocomputing, Jan. 2022, Vol. 470, Pages 389-404, DOI: doi.org/10.1016/j.neucom.2021.04.131


• C. Gallicchio. A. Micheli
  Architectural richness in deep reservoir computing.
  Neural Computing and Applications, Jan. 2022, Pages 1-18, DOI: doi.org/10.1007/s00521-021-06760-7


• D. Bacciu, A. Micheli, M. Podda.
  Edge-based sequential graph generation with recurrent neural networks .
  Neurocomputing, Oct. 2020, Vol. 416, Pages 177-189, DOI: doi.org/10.1016/j.neucom.2019.11.112


• D. Bacciu, F. Errica, A. Micheli, M. Podda.
  A gentle introduction to deep learning for graphs.
  Neural Networks, Sept. 2020, Vol. 129, Pages 203-221, DOI: 10.1016/j.neunet.2020.06.006


• D. Bacciu, F. Errica, A. Micheli.
  Probabilistic learning on graphs via contextual architectures .
  Journal of Machine Learning Research, Vol. 21 (134), Pages 1-39


• C. Gallicchio, A. Micheli.
  Deep Reservoir Neural Networks for Trees .
  Information Science, April 2019, Vol. 480, Pages 174-193, DOI: 10.1016/j.ins.2018.12.052


• M. Podda, D. Bacciu, A. Micheli, R. Bellu', G. Placidi, L. Gagliardi.
  A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor .
  Scientific Reports - Nature (2018) vol. 8:13743. DOI:10.1038/s41598-018-31920-6


• C. Gallicchio, A. Micheli, L. Pedrelli.
  Design of deep echo state networks .
  Neural Networks (2018), Vol. 108, December 2018, Pages 33-47, DOI: 10.1016/j.neunet.2018.08.002


• D. Bacciu, A. Micheli, A. Sperduti.
  Generative Kernels for Tree-Structured Data.
  IEEE Transactions on Neural Networks and Learning Systems,
  2018, Vol 29 (10), pp.4932-4946. ISSN: 2162-237X. DOI: 10.1109/TNNLS.2017.2785292


• C. Gallicchio, A. Micheli, L. Silvestri
  Local Lyapunov Exponents of Deep Echo State Networks.
  Neurocomputing. Vol. 298, pp. 34-45, Elsevier, 2018, DOI: 10.1016/j.neucom.2017.11.073, ISSN: 0925-2312


• C. Gallicchio, A. Micheli.
  Echo State Property of Deep Reservoir Computing Networks.
  Cognitive Computation, Springer
  Vol. 9(3), pp. 337-350 (2017), Special issue on "Advances in Biologically Inspired Reservoir Computing", DOI: 10.1007/s12559-017-9461-9, ISSN: 1866-9964


• C. Gallicchio, A. Micheli, L. Pedrelli.
  Deep Reservoir Computing: A Critical Experimental Analysis.
  Neurocomputing. Vol. 268, pp. 87-99, Elsevier, 2017, DOI: 10.1016/j.neucom.2016.12.089, ISSN: 0925-2312


• D. Bacciu, S. Chessa, C. Gallicchio, A. Micheli, L. Pedrelli, E. Ferro, L. Fortunati, D. La Rosa, F. Palumbo, F. Vozzi, O. Parodi.
  A Learning System for Automatic Berg Balance Scale Score Estimation.
  Engineering Applications of Artificial Intelligence, Elsevier Vol. 66, pp. 60-74, Elsevier, 2017, ISSN: 0952-1976, DOI: 10.1016/j.engappai.2017.08.018.


• S. Chessa, A. Micheli, R. Pucci, J. Hunter, G. Carroll, R. Harcourt.
  A Comparative Analysis of SVM and IDNN for Identifying Penguin Activities.
  Applied Artificial Intelligence.
  Vol. 31 (5-6), pp. 453-471. Taylor & Francis, 2017, ISSN: 0883-9514, DOI: 10.1080/08839514.2017.1378162


• F. Palumbo, D. La Rosa, E. Ferro, D. Bacciu, C. Gallicchio, A. Micheli, S. Chessa, F. Vozzi, O. Parodi.
  Reliability and human factors in Ambient Assisted Living environments: The DOREMI case study.
  Journal of Reliable Intelligent Environments , Springer.
  Vol. 3(3) pp. 139-157, Springer 2017, ISSN: 2199-4676, DOI: 10.1007/s40860-017-0042-1,


• R. Barbuti, S. Chessa, A. Micheli, R. Pucci.
  Localizing Tortoise Nests by Neural Networks.
  PLoS ONE.
  Vol. 11(3): e0151168. March 2016, DOI:10.1371/journal.pone.0151168


• F. Palumbo, C. Gallicchio, R. Pucci, A. Micheli.
  Human Activity Recognition using Multisensor Data Fusion based on Reservoir Computing.
  Journal of Ambient Intelligence and Smart Environments.
  IOS Press, vol. 8, pp. 87-107, March 2016, DOI:10.3233/AIS-160372, ISSN: 1876-1364.


• E. Crisostomi, C. Gallicchio, A. Micheli, M. Raugi, M. Tucci.
  Prediction of the Italian Electricity Price for Smart Grid Applications.
  Neurocomputing - Elsevier.
  Vol. 170, December 2015, Pages: 286-295. ISSN: 0925-2312 (Published on-line: June 2015). DOI: 10.1016/j.neucom.2015.02.089


• G. Amato, D. Bacciu, M. Broxvall, S. Chessa, S. Coleman, M. Di Rocco, M. Dragone, C. Gallicchio, C. Gennaro, H. Lozano, T.M. McGinnity,
  A. Micheli, A.K. Ray, A. Renteria, A. Saffiotti, D. Swords, C. Vairo, P. Vance.
  Robotic Ubiquitous Cognitive Ecology for Smart Homes .
  Journal of Intelligent & Robotic Systems - Springer.
  Volume 80(1), pp 57-81, Dec 2015, ISSN 0921-0296, DOI 10.1007/s10846-015-0178-2


• M. Dragone, G. Amato, D. Bacciu, S. Chessa, S. Coleman, M. Di Rocco, C. Gallicchio, C. Gennaro, H. Lozano, L. Maguire, M. McGinnity,
  A. Micheli, G. M.P. O.Hare, A. Renteria, A. Saffiotti, C. Vairo, P. Vance.
  A cognitive robotic ecology approach to self-configuring and evolving AAL systems.
  Engineering Applications of Artificial Intelligence - Elsevier.
  Vol. 45, October 2015, Pages: 269-280. ISSN: 0952-1976. DOI: 10.1016/j.engappai.2015.07.004


• D. Bacciu, P. Barsocchi, S. Chessa, C. Gallicchio A. Micheli.
  An experimental characterization of reservoir computing in ambient assisted living applications.
  Neural Computing & Applications - Springer-Verlag.
  Vol. 24(6), 2014, Pages: 1451- 1464. ISSN: 0941-0643. (Published on-line: March 2013). DOI: 10.1007/s00521-013-1364-4
  [LINK to data-set].


• D. Bacciu, A. Micheli, A. Sperduti.
  An Input-Output Hidden Markov Model for Tree Transductions.
  Neurocomputing - Elsevier.
  Vol. 112, 2013, Pages: 34 - 46. ISSN: 0925-2312. (Selected paper from 20th ESANN). DOI: 10.1016/j.neucom.2012.12.044


• D. Bacciu, A. Micheli, A. Sperduti.
  Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model.
  IEEE Transactions on Neural Networks and Learning Systems.
  Vol. 24 N. 2, February 2013, Pages: 231 - 247. ISSN: 2162-237X. DOI: 10.1109/TNNLS.2012.2228226
  [LINK to data-set].


• P. Barsocchi, S. Chessa, A. Micheli, C. Gallicchio.
  Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems
  ISPRS International Journal of Geo-Information - Springer.
  Vol. 2(4), Pages: 978- 995, 2013. ISSN: 2220-9964. DOI: 10.3390/ijgi2040978


• A. Micheli, F-M. Schleif, P. Tino.
  Novel approaches in machine learning and computational intelligence.
  Neurocomputing - Elsevier.
  Vol. 112, Pages: 1 - 3, 2013. ISSN: 0925-2312. DOI: 10.1016/j.neucom.2013.01.005
  (Editorial for the special issue: Advances in artificial neural networks, machine learning, and computational intelligence - from ESANN 2012).


• C. Bertinetto, C. Duce, R.Solaro, M.R. Tiné, A. Micheli, K. Héberger, A. Milicevic, S. Nikolic.
  Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods.
  MATCH (Communications in Mathematical and in Computer Chemistry).
  Vol.70(3), Pages 1005-1021, 2013. ISSN: 0340-6253.


• N. Di Mauro, P. Frasconi, F. Angiulli, D. Bacciu, M. de Gemmis, F. Esposito, N. Fanizzi, S. Ferilli, M.Gori, F.A.Lisi, P. Lops, D. Malerba,
  A. Micheli, M.Pelillo, F. Ricci, F. Riguzzi, L. Saitta, G. Semeraro.
  Italian Machine Learning and Data Mining research: The last years.
  Intelligenza Artificiale.
  Vol.7(2), 2013, Pages: 77- 89, IOS Press, ISSN: 1724-8035. DOI: 10.3233/IA-130050


• C. Gallicchio, A. Micheli.
  Tree Echo State Networks.
  Neurocomputing - Elsevier.
  Vol. 101, Pages 319-337. Available online 25 September 2012, ISSN: 0925-2312. DOI: 10.1016/j.neucom.2012.08.017


• D. Bacciu, A. Micheli, A. Sperduti.
  Compositional Generative Mapping for Tree-Structured Data - Part I: Bottom-Up Probabilistic Modeling of Trees.
  IEEE Transactions on Neural Networks and Learning Systems.
  Vol. 23 N. 12, December 2012, Pages: 1987 - 2002. ISSN: 2162-237X. DOI: 10.1109/TNNLS.2012.2222044
  [LINK to data-set].


• R. Barbuti, S. Chessa, A. Micheli, D. Pallini, R. Pucci, G. Anastasi.
  Tortoise@: a system for localizing tortoises during the eggs deposition phase.
  Atti della Società Toscana di Scienze Naturali - Memorie, Serie B.
  Vol.119, Pages 89 - 95, 2012. Elsevier. ISSN: 0365-7450. DOI: 10.2424/ASTSN.M.2012.13


• C. Gallicchio, A. Micheli.
  Architectural and Markovian factors of echo state networks.
  Neural Networks.
  Vol.24, Issue 5, Pages 440-456, June 2011. Elsevier. ISSN: 0893-6080. DOI: 10.1016/j.neunet.2011.02.002


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, A. Starita, M. R. Tiné.
  QSPR analysis of copolymers by recursive neural networks: Prediction of the glass transition temperature of (meth)acrylic random copolymers.
  Molecular Informatics (formerly "QSAR & Combinatorial Science").
  Vol.29, Issue 8-9, Pages 635-643, September 2010. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim (Impact Factor: 3.027). ISSN: 1868-1743. DOI:10.1002/minf.201000079.


• L. Bernazzani, C. Duce, A. Micheli, V. Mollica, M. R. Tiné.
  Quantitative Structure-Property Relationship (QSPR) Prediction of Solvation Gibbs Energy of Bifunctional Compounds by Recursive Neural Networks.
  Journal of Chemical & Engineering Data.
  Vol. 55, Issue 12, Pages 5425-5428, December 2010. ACS Publications Washington, DC (2010 Impact Factor: 2.089). ISSN: 0021-9568. DOI: 10.1021/je100535p


• A. Micheli.
  Neural Network for Graphs: A Contextual Constructive Approach.
  IEEE Transactions on Neural Networks.
  Vol. 20, n. 3, Pages 498-511, March 2009. IEEE Inc. ISSN 1045-9227.


• L. Nicotra, A. Micheli.
  Modeling Adaptive Kernels from Probabilistic Phylogenetic Trees.
  Artificial Intelligence in Medicine (Journal).
  Vol. 45, Issues 2-3, Pages 125-134, Special Issue on Computational Intelligence and Machine Learning in Bioinformatics, March 2009. Imprint: Elsevier. ISSN 0933-3657.


• C. Duce, A. Micheli, R. Solaro, A. Starita, M. R. Tiné.
  Recursive neural networks prediction of glass transition temperature from monomer structure. An application to acrylic and methacrylic polymers.
  Journal of Mathematical Chemistry.
  Vol. 46, n. 3, Pages 729-755, October 2009. Publisher: Springer (Impact Factor: 1.435). ISSN 0259-9791.


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, A. Starita, M. R. Tiné.
  Evaluation of Hierarchical Structured Representations for QSPR Studies of Small Molecules and Polymers by Recursive Neural Networks.
  Journal of Molecular Graphics and Modelling.
  Vol.27(7), Pages 797-802, 2009. Imprint: Elsevier (ISI Impact Factor 2.033). ISSN 1093-3263.


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, M. R. Tiné.
  Toxicity Prediction by a Cheminformatics Approach.
  La chimica e l'industria. Vol. 27(9), p. 136-143, 2009. Promedia publishing. ISSN: 0009-4315


• R. Bini, C. Chiappe, C. Duce, A. Micheli, R. Solaro, A. Starita, M. R. Tiné.
  Ionic Liquids: Prediction of their Melting Points by a Recursive Neural Network Model.
  Green Chemistry (Journal).
  RSC Publishing, Vol. 10, Issue 3, Pages 306-309, 2008 (published on the web: 08 January 2008) DOI:10.1039/b708123e. (Impact Factor 4.192) ISSN 1463-9262


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, A. Starita, M. R. Tiné.
  Prediction of the Glass Transition Temperature of (Meth)Acrylic Polymers Containing Phenyl Groups by Recursive Neural Network.
  Polymer (Journal).
  Vol. 48, Issue 24, Pages 7121-7129, 16 November 2007, Elsevier Ltd (Amsterdam, The Netherlands) (Impact factor 2.773). ISSN: 0032-3861


• A. Micheli, A. Sperduti, A. Starita.
  An Introduction to Recursive Neural Networks and Kernel Methods for Cheminformatics.
  Current Pharmaceutical Design (Journal). Vol. 13, Num. 14, Pages 1469-1495, May 2007,
  Special Issue on "Ground-Breaking Mathematical Models for Basic and Applied Research", Executive Editors: A.O. Vassilev, H.E. Tibbles.
  Bentham Science Publishers Ltd. (ISI Impact Factor 5.270). ISSN: 1381-6128.


• M. Cipollini, J. He, P. Rossi, F. Baronti, A. Micheli, A.M. Rossi, R. Barale.
  Can individual repair kinetics of UVC-induced DNA damage in human lymphocytes be assessed through comet assay?
  Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis.
  Vol. 601, Issues 1-2, 10 October 2006 (Available online August 2006), Pages 150-161 (Impact factor: 4.111), 2006 Elsevier B.V. ISSN: 0027-5107.


• L. Bernazzani, C. Duce, A. Micheli, V. Mollica, A. Sperduti, A. Starita, M. R. Tiné.
  Predicting Physical Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks.
  Journal of Chemical Information and Modeling (formerly Journal of Chemical Information and Computer Sciences).
  ACS Publications. Washington, DC. (ISI Impact Factor 3.423). Vol. 46(5): 2030-2042, September 2006. ACS Publications. ISSN: 1549-9596. DOI 10.1021/ci060104e.


• C. Duce, A. Micheli, A. Starita, M.R. Tiné, R. Solaro.
  Prediction of Polymer Properties from their Structure by Recursive Neural Networks.
  Macromolecular Rapid Communications (Journal), Vol. 27(9): 711-715, May 2006. Wiley-VCH Verlag GmbH & Co., Weinheim, Germany.
  (ISI Impact Factor 3.366). Online ISSN: 1521-3927, Print ISSN: 1022-1336.
  


• C. Duce, A. Micheli, R. Solaro, A. Starita, M.R. Tiné.
  Prediction of Chemical-Physical Properties by Neural Networks for Structures.
  Macromolecular Symposia (Journal). Vol. 234(1): 13-19, February 2006. Wiley-VCH Verlag GmbH & Co., Weinheim, Germany.
  (ISI Impact Factor 0.691). Online ISSN: 1521-3900, Print ISSN: 1022-1360.


• B. Hammer, A. Micheli, A. Sperduti.
  Universal Approximation Capability of Cascade Correlation for Structures.
  Neural Computation.
  Vol. 17, Issue 5, Pages 1109-1159, May 2005 (ISI Impact factor 2.313), MIT press. ISSN 0899-7667. DOI 10.1162/0899766053491878


• A. Micheli, F. Portera, A. Sperduti.
  A Preliminary Empirical Comparison of Recursive Neural Networks and Tree Kernel Methods on Regression Tasks for Tree Structured Domains.
  Neurocomputing, Elsevier.
  Volume 64, Pages 73-92, March 2005 (Selected from 12-th ESANN 2004). 2005 Elsevier B.V, ISSN 0925-2312.


• B. Hammer, A. Micheli, A. Sperduti, M. Strickert.
  Recursive Self-organizing Network Models.
  Neural Networks, Elsevier Science.
  Vol. 17, Issues 8-9, Pages 1061-1085, October-November 2004, Available online since 8 October 2004 (www.sciencedirect.com) © 2004 Published by Elsevier Ltd. Imprint: Pergamon. ISSN 0893-6080. DOI: 10.1016/j.neunet.2004.06.009


• A. Micheli, D. Sona, A. Sperduti.
  Contextual Processing of Structured Data by Recursive Cascade Correlation.
  IEEE Transactions on Neural Networks.
  Vol. 15, n. 6, Pages 1396- 1410, November 2004. ISSN 1045-9227. DOI 0.1109/TNN.2004.837783.


• B. Hammer, A. Micheli, A. Sperduti, M. Strickert.
  A General Framework for Unsupervised Processing of Structured Data.
  Neurocomputing, Elsevier.
  Volume 57, Pages 3-35, March 2004. Imprint: Elsevier (Selected from the contribution at ESANN2002) ISSN 0925-2312. DOI 10.1016/j.neucom.2004.01.008


• A. Micheli, A. Sperduti, A. Starita, A.M. Bianucci.
  Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines.
  Journal of Chemical Information and Computer Sciences.
  (ACS Publications, 1155 16th St., N.W. Washington, DC 20036), Vol. 41(1): 202-218, January 2001. (ISI Impact Factor 2.810). ISSN 0095-2338.


• A.M. Bianucci, A. Micheli, A. Sperduti, A. Starita.
  Application of Cascade Correlation Networks for Structures to Chemistry.
  Applied Intelligence Journal (Kluwer Academic Publishers).
  Special Issue on "Neural Networks and Structured Knowledge" Vol. 12 (1/2): 117-146, January 2000. ISSN 0924-669X.

Book Chapters

• A. Micheli, M. Podda.
  Deep Learning in Cheminformatics.
  In book: Deep Learning In Biology And Medicine, vol 896, pp. 157-195.
  Feb. 2022. ISBN 978-1-80061-093-4. DOI: doi.org/10.1142/9781800610941_0006


• C. Gallcchio, A. Micheli.
  Deep reservoir computing.
  In book: Reservoir Computing: Theory, Physical Implementations, and Applications, vol 896, pp. 77-95.
  Springer 2021. ISBN 978-981-13-1686-9. DOI: doi.org/10.1007/978-981-13-1687-6_4


• D. Bacciu, A. Micheli.
  Deep Learning for Graphs.
  In book: Recent Trends in Learning From Data. Studies in Computational Intelligence, vol 896, pp. 99-127.
  Springer, Cham. April 2020. ISBN 978-3-030-43882-1. DOI: 10.1007/978-3-030-43883-8_5


• A. Micheli, C. Bertinetto, C. Duce, R. Solaro, M.R. Tiné.
  Recursive Neural Networks for Cheminformatics: QSPR for Polymeric Compounds (Towards Biomaterial Design).
  In book: Computational Intelligence and Bioengineering, Book Series: Frontiers in Artificial Intelligence and Applications - Knowledge-Based Intelligent Engineering Systems (FAIA-KBIES). Vol. 196, Pages 37-52, IOS Press, Netherlands (c) 2009. ISSN: 0922-6389. ISBN 978-1-60750-010-0. DOI: 10.3233/978-1-60750-010-0-37


• B. Hammer, A. Micheli, A. Sperduti. Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.
  Chapter in Book: "Perspectives of Neural-Symbolic Integration", pp. 67-94. Editors: B. Hammer, P. Hitzler, Series Ed.: J. Kacprzyk, Springer series: "Studies in Computational Intelligence", Volume 77, November 2007, Springer Verlag (Berlin / Heidelberg). ISSN 1860-949X. ISBN 978-3-540-73953-1


• F. Baronti, A. Micheli, A. Passaro, A. Starita.
  Machine Learning Contribution to Solve Prognosis Medical Problems.
  Chapter in Book: "Outcome Prediction in Cancer". pp. 261-283. Editors: A.F.G. Taktak and A.C. Fisher, Imprint: Elsevier Science. Publication Date: November 2006. For North America © 2007 Elsevier B.V. ISBN 0-444-52855-5.


• A. Micheli, A. Sperduti, A. Starita, A.M. Bianucci.
  A Novel Approach to QSPR/QSAR Based on Neural Networks for Structures.
  Chapter in Book: "Soft Computing Approaches in Chemistry". Book Series: Studies in Fuzziness and Soft Computing, pp. 265-296, H. M. Cartwright, L. M. Sztandera, Eds., Published by Springer-Verlag, Heidelberg. March 2003. ISBN 3-540-00245-6.

Book (Editing)

• F. Masulli, A. Micheli, A. Sperduti (Editors).
  Computational Intelligence And Bioengineering, Essays in Memory of Antonina Starita,
  Book (including the preface at pages V-IX by F. Masulli, A. Micheli, A. Sperduti). Book Series: Frontiers in Artificial Intelligence and Applications - Knowledge-Based Intelligent Engineering Systems (FAIA-KBIES), Vol. 196, IOS Press, Netherlands (c) 2009. ISBN 978-1-60750-010-0.

Proceedings (selected, only up to 2017)

• C. Gallicchio, J. D. Martin-Guerrero, A. Micheli, E. Soria-Olivas, Randomized Machine Learning Approaches: Recent Developments and Challenges, Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), Bruges, Belgium, 26-28 April 2017, i6doc.com, pp. 77-86, ISBN: 978-287587038-4


• C. Gallicchio, A. Micheli, L. Silvestri, Local Lyapunov Exponents of Deep RNN, Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), Bruges, Belgium, 26-28 April 2017, i6doc.com, pp. 559-564, ISBN: 978-287587038-4.


• G. Amato, D. Bacciu, S. Chessa, M. Dragone, C. Gallicchio, C. Gennaro, H. Lozano, A. Micheli, G. M. P. O’Hare, A. Renteria, C. Vairo, A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living, Proceedings of the 7th International Symposium on Ambient Intelligence (ISAmI), Ambient Intelligence - Software and Applications, Sevilla, Spain, 1-3 June 2016, Advances in Intelligent Systems and Computing Series, Springer 2016, vol. 476, pp. 1-9, DOI:10.1007/978-3-319-40114-0_1, ISBN: 978-3-319-40113-3


• D. Bacciu, S. Chessa, E. Ferro, L. Fortunati, C. Gallicchio, D. La Rosa, M. Llorente, A. Micheli, F. Palumbo, O. Parodi, A. Valenti, F. Vozzi, Detecting Socialization Events in Ageing People: The Experience of the DOREMI Project, Proceedings of the 12th International Conference on Intelligent Environments (IE), London, United Kingdom, 14-16 September 2016, IEEE 2016, pp. 132-135, ISBN: 978-1-5090-4056-8


• C. Gallicchio, A. Micheli, L. Pedrelli, L. Fortunati, F. Vozzi, O. Parodi, A reservoir computing approach for balance assessment, in A. Douzal-Chouakria, J.A. Vilar, and P.-F. Marteau, editors, Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers, volume 9785 of Lecture Notes in Computer Science (LNCS), pp 65-77, Springer International Publishing, 2016. DOI: 10.1007/978-3-319-44412-3_5, ISBN: 978-3-319-44412-3.


• C. Gallicchio, A. Micheli, A Reservoir Computing Approach for Human Gesture Recognition from Kinect Data, Proceedings of the Workshop Artificial Intelligence for Ambient Assisted Living (AI*AAL 2016), co-located with the 15th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2016), Genoa, Italy, November 28, 2016, S. Bandini, G. Cortellessa, F. Palumbo Editors, CEUR Workshop Proceedings, vol. 1803, pp. 33-42, March 2017. ISSN 1613-0073


• C. Gallicchio, A. Micheli, Deep Reservoir Computing: A Critical Analysis. Accepted - ESANN 2016 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 27-29 April 2016, i6doc.com 2016, pp. 497-502, ISBN: 978-287587026-1


• D. Bacciu, C. Gallicchio, A. Micheli, A reservoir activation kernel for trees. Accepted - ESANN 2016 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 27-29 April 2016, i6doc.com 2016, pp. 29-34, ISBN: 978-287587026-1


• M. Dragone, C. Gallicchio, R. Guzman, A. Micheli, RSS-based Robot Localization in Critical Environments using Reservoir Computing. Accepted - ESANN 2016 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 27-29 April 2016, i6doc.com 2016, pp. 71-76, ISBN: 978-287587026-1


• C. Gallicchio, A. Micheli, O. Parodi, L. Pedrelli, F. Vozzi, Preliminary Experimental Analysis of Reservoir Computing Approach for Balance Assessment. Proceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2015 - Workshop co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015, Vol-1425, pages 57-62, CEUR Workshop Proceedings, ISSN 1613-0073.


• M. Danelutto, C. Gallicchio, A. Micheli, D. Virgilio, M. Torquati. Structured parallel implementation of Tree Echo State Network validation process, Proceedings of the International Conference on Parallel Computing (ParCo), Edinburgh, Scotland, 1-4 September 2015, Parallel Computing: On the Road to Exascale, Advances in Parallel Computing Series, G.R. Joubert, H. Leather, M. Parsons, F. Peters, M. Sawyer Editors, IOS Press, 2016, Volume 27, pp. 145-154, DOI: 10.3233/978-1-61499-621-7-145, ISBN: 978-1-61499-620-0


• D. Bacciu, F. Benedetti, A. Micheli, ESNigma: efficient feature selection for Echo State Networks" , Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), Eds. M. Verleysen, pp. 189-194, ISBN 978-287-587-014-8, i6doc, 2015


• D. Bacciu, S. Chessa, C. Gallicchio, A. Micheli, E. Ferro, L. Fortunati, F. Palumbo, O. Parodi, F. Vozzi, S. Hanke, J. Kropf, K. Kreiner, Smart Environments and Context-Awareness for Lifestyle Management in a Healthy Active Ageing Framework. Thematic track Artificial Intelligence in Medicine (AIM@EPIA-2015) Proceedings of the 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015, Progress in Artificial Intelligence, Lecture Notes in Computer Science (LNCS), Springer International Publishing, vol. 9273, pag. 54-66, ISSN 0302-9743, ISBN 978-3-319-23484-7


• C. Gallicchio, A. Micheli. A Preliminary Application of Echo State Networks to Emotion Recognition. Proceedings of the First Italian Conference on Computational Linguistics CLiC-it 2014 & the Fourth International Workshop EVALITA 2014, Volume II, 11th December 2014 Pisa, Italy, pp 116-119, Pisa University Press, ISBN 978-886741-472-7, DOI 10.12871/clicit2014221


• D. Bacciu, A. Micheli, A. Sperduti. Integrating Bi-directional Contexts in a Generative Kernel for Trees. Proceedings of the IJCNN-WCCI IEEE World Congress on Computational Intelligence - July 6-11, 2014, Beijing, China, pp. 4145- 4151, IEEE 2014. ISBN 9781479966271, DOI: 10.1109/IJCNN.2014.6889768


• D. Bacciu, A. Micheli, A. Sperduti. Modeling Bi-Directional Tree Contexts by Generative Transductions. Proceedings of the ICONIP2014 - 21st International Conference on Neural Information Processing, 3-6 November 2014, Kuching, Sarawak, Malaysia, "Neural Information Processing", Part I, Lecture Notes in Computer Science (LNCS) series, Vol. 8834, part I, pp. 543-550, 2014. Springer 2014. ISSN 0302-9743 ISBN: 978-3-319-12636-4, DOI 10.1007/978-3-319-12637-1_68


• D. Bacciu, C. Gallicchio, A. Micheli, M. Di Rocco, A. Saffiotti. Learning Context-Aware Mobile Robot Navigation in Home Environments. P roceedings of the IISA 2014 - 5th IEEE International Conference on Information, Intelligence, Systems and Applications. July 07-09, 2014, Chania Crete, Greece, pp. 57-62, IEEE 2014. ISBN 9781479961719. DOI 10.1109/IISA.2014.6878733


• S. Chessa, C. Gallicchio, R. Guzman, A. Micheli. Robot Localization by Echo State Networks Using RSS . In: Recent Advances of Neural Networks Models and Applications. Proceedings of the 23rd Workshop of the Italian Neural Networks Society (SIREN), May 23-25, 2013, Vietri sul Mare, Salerno, Italy. Editors: Simone Bassis, Anna Esposito, Francesco Carlo Morabito. Series: Smart Innovation, Systems and Technologies Vol. 26, pp. 147-154. Springer 2014. ISSN 2190-3018 ISBN 978-3-319-04128-5. DOI 10.1007/978-3-319-04129-2


• R. Barbuti, S. Chessa, A. Micheli, R. Pucci. Identification of nesting phase in tortoise populations by neural networks. In Proceedings (Selected papers) of the 50th Annual Convention of the Artificial Intelligence and the Simulation of Behavior, 2014 (AISB-50), ISAWEL (1^st Symposium on Intelligent Systems for Animal Welfare), Goldsmiths University of London, London, UK, April 1st-4th 2014. Pages 62-65. Publised by the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB). ISBN: 978-1-908187-42-0


• F. Palumbo, P. Barsocchi, C. Galliccio, S. Chessa, A. Micheli. Multisensor Data Fusion for Activity Recognition Based on Reservoir Computing. In Communications in Computer and Information Science (CCIS) series - Evaluating AAL Systems Through Competitive Benchmarking (EvAAL 2013), J.A. Botia et al. (Eds.), Vol. 386, pp. 24-35, 2013. (c) Springer-Verlag Berlin Heidelberg 2013, ISSN 1865-0929, ISBN 978-3-642-41042-0, DOI 10.1007/978-3-642-41043-7_3


• D. Bacciu, C. Gallicchio, A. Lenzi, S. Chessa, A. Micheli, S. Pelagatti, C. Vairo. Distributed Neural Computation over WSN in Ambient Intelligence, Proceedings of the 4th International Symposium on Ambient Intelligence (ISAmI'13), Salamanca, Spain, 22nd - 24th May, 2013. In Advances in Intelligent Systems and Computing series, Vol. 219, pp.147-154, Springer, 2013. ISSN 2194-5357, ISBN 978-3-319-00565-2, DOI 10.1007/978-3-319-00566-9_19.


• D. Bacciu, S. Chessa, C. Gallicchio, A. Micheli, P. Barsocchi. An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living. 22nd Italian Workshop on Neural Nets, WIRN 2012, May 17-19, Vietri sul Mare, Salerno, Italy. In Neural Nets and Surroundings, Series: Smart Innovation, Systems and Technologies, Vol. 19 pp. 41-50, 2013, Springer-Verlag. ISSN 2190-3018. ISBN 978-3-642-35466-3, DOI: 10.1007/978-3-642-35467-0_5


• D. Bacciu, A. Micheli, A. Sperduti. Input-Output Hidden Markov Models for Trees, Proceedings of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 25-27 April 2012, Editor: Michel Verleysen. Ciaco scrl i6doc.com, Belgique, pp. 25-30, ISBN: 978-2-87419-047-6. Note: Selected for an extended contribution to the special issue in the journal: Neurocomputing -Elsevier.


• C. Gallicchio, A. Micheli, G. Visco. Constructive Reservoir Computation with Output Feedbacks for Structured Domains, Proceedings of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 25-27 April 2012, Editor: Michel Verleysen. Ciaco scrl - i6doc.com, Belgique, pp. 31-36, ISBN: 978-2-87419-047-6.


• D. Bacciu, S. Chessa, C. Gallicchio, A. Lenzi, A. Micheli, S. Pelagatti. A General Purpose Distributed Learning Model for Robotic Ecologies, In Robot Control - 10th International IFAC Symposium on Robot Control, SYROCO 2012. Dubrovnik, Croatia on September 05-07, 2012, Vol.10(1), pp. 435-440, Elsevier Science BV, ISSN: 1474-6670, ISBN: 978-3-902823-11-3, DOI 10.3182/20120905-3-HR-2030.00178.


• G. Amato, M. Broxvall, S. Chessa, M. Dragone, C. Gennaro, R. López, L. Maguire, T.M. Mcginnity, A. Micheli, A. Renteria, G.M.P. O'Hare, F. Pecora. Robotic UBIquitous COgnitive Network. Ambient Intelligence - Software and Applications - 3rd International Symposium on Ambient Intelligence (ISAmI 2012), Salamanca (Spain), 28-30th March, 2012. In Advances in Intelligent and Soft Computing (AISC) series, Vol. 153, pp. 191-195, Springer-Verlag, 2012. ISSN 1867-5662, ISBN: 978-3-642-28782-4, DOI 10.1007/978-3-642-28783-1_23.


• D. Bacciu, A. Micheli, A. Sperduti. A Generative Multiset Kernel for Structured Data. 22nd International Conference on Artificial Neural Networks and Machine Learning - ICANN 2012, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Springer Lecture Notes in Computer Science (LNCS) series. A.E.P. Villa et al. (Eds.): LNCS Vol. 7552 (I), pp. 57-64, 2012. (c) Springer-Verlag Berlin Heidelberg 2012, ISSN: 0302-9743 , ISBN: 978-3-642-33268-5, DOI: 10.1007/978-3-642-33269-2_8


• C. Gallicchio, A. Micheli, P. Barsocchi, S. Chessa. User Movements Forecasting by Reservoir Computing using Signal Streams produced by Mote-Class Sensors. Proceedings of the International ICST Conference on Mobile Lightweight Wireless Systems (Mobilight), 9-11 May 2011, Bilbao, Spain. In "Mobile Lightweight Wireless Systems", Springer's Lecture Notes of ICST, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), Vol. 81, Part 3, pp. 151-168, Springer, 2012. ISSN 1867-8211, ISBN 978-3-642-29478-5, DOI: 10.1007/978-3-642-29479-2_12


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, M. R. Tiné. Adaptive Modelling of Structured Molecular Representations for Toxicity Prediction. In AIP Conference Proceedings, International Conference on Computational Methods in Science and Engineering, Rhodes, Greece, 2009, Vol. 1504, pp.721-724, Published 2012, American Institute of Physics, New York. ISSN: 0094-243X, ISBN: 9780735411227, DOI: 10.1063/1.4771796.


• D. Bacciu, A. Micheli, A. Sperduti. Adaptive Tree Kernel by Multinomial Generative Topographic Mapping. Proceedings of the 2011 IEEE International Joint Conference on Neural Networks (IJCNN'11), San Jose, CA, USA, July 31- August 5 2011, Pages 1651-1658, IEEE. ISSN: 2161-4393, Print ISBN: 978-1-4244-9635-8. DOI: 10.1109/IJCNN.2011.6033423


• D. Bacciu, C. Gallicchio, A. Micheli, P. Barocchi, S. Chessa. Predicting user movements in heterogeneous indoor environments by reservoir computing. Proceedings of the IJCAI (International Joint Conference on Artificial Intelligence) - Workshop on Space, Time and Ambient Intelligence (STAMI), Barcellona, Spain, July 2011, M. Bhatt, H. W. Guesgen, and J. C. Augusto, editors, pages 1- 6, 2011.


• C. Gallicchio, A. Micheli. Supervised state mapping of clustered GraphESN states. Proceedings of the 21th Italian Workshop on Neural Networks (Frontiers in Artificial Intelligence and Applications), WIRN June 3-5 2011, Vietri sul Mare, Salerno, Italy. Imprint: IOS press. Vol. 234, Pages 28-35, Dicembre 2011. IOS press. ISSN 0922-6389, ISBN: 978-1607509714. DOI: 10.3233/978-1-60750-972-1-28


• C. Gallicchio, A. Micheli. Exploiting Vertices States in GraphESN byWeighted Nearest Neighbor. Proceedings of the 19^th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 27-29 April 2011, Editor: Michel Verleysen. Ciaco scrl - i6doc.com, Belgique, pp. 375-380, ISBN/ISSN: 978-2-87419-044-5.


• D. Bacciu, A. Micheli, A. Sperduti. Bottom-Up Generative Modeling of Tree-Structured Data. Proceedings of 17^th International Conference, ICONIP (International Conference on Neural Information Processing) 2010, Sydney, Australia, November 2010 "Neural Information Processing. Theory and Algorithms": Lecture Notes in Computer Science (LNCS), Volume 6443, pp. 660-668, Springer Berlin / Heidelberg Germany, 2010. Editors: Kevin K. W. Wong, B. Sumudu U. Mendis, Abdesselam Bouzerdoum. ISSN: 0302-9743. ISBN 3-642-17536-8. DOI 10.1007/978-3-642-17537-4_80


• C. Gallicchio, A. Micheli. Graph Echo State Networks. Proceedings of the 2010 IEEE International Joint Conference on Neural Networks (IJCNN'10) at the WCCI 2010 (World Congress on Computational Intelligence), Barcelona, Spain, 18-23 July 2010. IEEE, pp. 2159-2166, ISBN/ISSN: 978-1-4244-8126-2, doi: 10.1109/IJCNN.2010.5596796.


• D. Bacciu, A. Micheli, A. Sperduti. Compositional Generative Mapping of Structured Data. Proceedings of the 2010 IEEE International Joint Conference on Neural Networks (IJCNN'10) at the WCCI 2010 (World Congress on Computational Intelligence), Barcelona, Spain, 18-23 July 2010. IEEE, pp. 1359-1366, ISBN/ISSN: 978-1-4244-8126-2, doi:10.1109/IJCNN.2010.5596606.


• C. Gallicchio, A. Micheli. A Markovian Characterization of Redundancy in Echo State Networks by PCA. Proceedings of the 18^th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 28-30 April 2010, Editor: Michel Verleysen, EVERE: d-side, p. 321-326, ISBN/ISSN: 2-930307-10-2.


• C. Gallicchio, A. Micheli. TreeESN: a Preliminary Experimental Analysis. Proceedings of the 18^th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Bruges, Belgium, 28-30 April 2010, Editor: Michel Verleysen, EVERE: d-side, pp. 333-338, ISBN/ISSN: 2-930307-10-2.


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, M. R. Tiné, S. Nikolic, K. Heberger. Recursive neural network modelling of benzene derivatives acute toxicity. Riassunto di comunicazione a congresso internazionale MATH/CHEM/COMP 2009: The 24th International Course & Conference on the Interfaces among Mathematics, Chemistry & Computer Sciences, June 08-13, 2009, Dubrovnik, Croatia. Pag. 8. ISBN: 978-953-6954-51-3.


• C.G. Bertinetto, C. Duce, A. Micheli, A. Starita, R. Solaro, M. R. Tiné. Modelling Structure-Property Relationship for Copolymers by Structured Representation of Repeating Units, Computational Methods in Science and Engineering, Advances in Computational Science edited by T. E. Simos and G. Maroulis (ICCMSE 2008), AIP Conference Proceedings, Vol. 1148 (2), pp 400-403, Published August 2009, American Institute of Physics, ISBN 978-0-7354-0685-8. Vincitore del premio "Young Scientist Excellence Award" dell'ICCMSE 2008.


• A. Micheli, A. Sperduti. Recursive Principal Component Analysis of Graphs, Proceedings of ICANN 07 - International Conference on Artificial Neural Networks, 9-13 September 2007, Porto, Portugal. Lecture Notes in Computer Science LNCS, Vol. 4669/2007, pp. 826-835, Springer Berlin / Heidelberg 2007. ISBN 978-3-540-74693-5.


• L. Nicotra, A. Micheli, S. Starita. Generative Kernels for Gene Function Prediction through Probabilistic Tree Models of Evolution, CIBB 2007 - Fourth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Portofino Vetta, Ruta di Camogli (Genova), Italy, July 7-10, 2007. In Book: Applications of Fuzzy Sets Theory. Proceedings of 7th International Workshop on Fuzzy Logic and Applications, WILF 2007. Editors F. Masulli, S. Mitra, G. Pasi. Lecture Notes in Computer Science LNCS, Vol. 4578/2007, pp. 512-519, Springer Berlin / Heidelberg 2007. ISSN 0302-9743. ISBN 978-3-540-73399-7.


• D. Bacciu, A. Micheli, A. Starita. Simultaneous clustering and feature ranking by competitive repetition suppression learning with application to gene data analysis, Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED 2007), 25-27 July 2007, University of Plymouth, Plymouth, U.K. pp. 1-8, (abstract in Book of Abstract pp.35) Copyright © 2007 University of Plymouth, Drake Circus, Plymouth PL4 8AA UK. ISBN: 978-1-84102-176-8.


• C. Bertinetto, R. Bini, C. Chiappe, C. Duce, A. Micheli, R. Solaro, A. Starita, M.R. Tiné. Recent Advances in the Representation of Molecular Structures for RecNN-QSPR Analysis, Selected paper from the International Conference of Computational Methods in Sciences and Engineering - ICCMSE 2006, Loutraki, Korinthos, Greece 27 October- 1 November 2006, published in Lecture Series on Computer and Computational Sciences (LSCCS), Volume 7, 2006, pp. 1352-1355. Editors: T. Simos, G. Maroulis. Brill Academic Publishers, Leiden, The Netherlands. ISBN10: 90 04 15542 2, ISBN13: 978-90-04-15542-8.


• A. Micheli, A.S. Sestito. A New Neural Network Model for Contextual Processing of Graphs, Neural Nets: 16th Italian Workshop on Neural Nets, WIRN 2005. Vietri sul Mare, Italy, June 8-11, 2005. Revised Selected Papers. Editors: Apolloni, Marinaro, Nicosia, Tagliaferro. Lecture Notes in Computer Science, LNCS, Vol. 3931, pp. 10-17, Mar 2006, Springer 2006, ISBN 3-540-33183-2. ISSN 0302-9743


• A. Micheli, A.S. Sestito. Dealing with Graphs by Neural Networks, 16th European Conference on Machine Learning (ECML) and 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) 3-7 October 2005 - Porto, Portugal - Proceedings of the ECML 2005 Workshop on "Sub-symbolic paradigms for learning in structured domains", pp 66-75. Editors: M. Gori, P. Avesani. Porto, Portugal, October 2005.


• F. Baronti, F. Colla, V. Maggini, A. Micheli, A. Passaro, A.M. Rossi, A. Starita. Experimental Comparison of Machine Learning Approaches to Medical Domains: A case study of genotype influence on oral cancer development, Proceedings of EACDA 2005 - European Conference on Emergent Aspects in Clinical Data Analysis, September 28 - 30 2005, Pisa, Italy, pp. 81-86, D30- BioPattern, 2006.


• A. Micheli, A. Starita, C. Duce, R. Solaro, M.R. Tiné. Approaches to Cheminformatics and Biomaterials Design by Neural Networks for Structures: first applications to small molecules and polymers, Proceedings of EACDA 2005 - European Conference on Emergent Aspects in Clinical Data Analysis, September 28 - 30 2005, Pisa, Italy, pp. 13-20, D30- BioPattern, 2006.


• C. Duce, A. Micheli, R. Solaro, A. Starita, M.R. Tiné. Recursive Neural Networks for Quantitative Structure-Property Relationship Analysis of Polymers, Selected papers from the International Conference of Computational Methods in Sciences and Engineering - ICCMSE 2005, Loutraki, Korinthos, Greece 21-26 October 2005 published in Lecture Series on Computer and Computational Sciences (LSCCS), Volume 4, 2005, pp. 1546-1549. Editors: T. Simos, G. Maroulis. Brill Academic Publishers, Leiden, The Netherlands. ISBN 90-6764-444-7.


• C. Duce, R. Solaro, M.R. Tiné A. Micheli, A. Starita. A QSAR - Neural Networks Approach to the Prediction of Polymer Properties, MEDICTA 2005 7th Mediterranean Conference on Calorimetry and Thermal Analysis, 2-6 July 2005, Thessaloniki, Greece, p.74, Publishing ZITI (Greece) Perea- Thessaloniki, June 2005. ISBN 960-88724-1-3.


• A. Passaro, F. Baronti, V. Maggini, A. Micheli, , A.M.Rossi, A. Starita. Exploring Relationships between Genotype and Oral Cancer Development Through XCS, In Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation (Washington, D.C., June 25 - 26, 2005). GECCO '05, pp.147-151. ACM Press, New York, NY. ISBN:0-123-45699-9.


• F. Baronti, V.Maggini, A. Micheli, A. Passaro, A.M. Rossi, A. Starita. A Preliminary Investigation on Connecting Genotype to Oral Cancer Development through XCS, CIBB 2004 - International Meeting On Computational Intelligence Methods For Bioinformatics And Biostatistics. Special session of WIRN 04 - XV Italian Workshop On Neural Networks. September 14-15, 2004, Perugia, ITALY. Abstract published in: Atti della Conferenza Italiana sui Sistemi Intelligenti 2004, Perugia 14-17 September 2004, p. 102 A. Milani ed., © 2004 Morlacchi Editore, Perugia.ISBN 88-89422-09-2. Paper published in "Biological and Artificial Intelligence Environments" B. Apolloni, M. Marinaro, R. Tagliaferro (Eds.), pp. 11-20, Publisher: Springer, Heidelberg. 2005. ISBN 1-4020-3431-8.


• V. Maggini, A. Abbondandolo, R. Barale, F. Baronti, S. Bonatti, F. Canzian, G. Casartelli, L. Guidi, G. Margarino, P. Mereu, A. Micheli, A. Passaro, A.M. Rossi, A. Starita. Computational Intelligence Methods for Data Analysis: A Case-control Study on Genetic Susceptibility in Squamous Cell Carcinoma of the Head and Neck (HNSCC), 46th Annual Meeting of the Italian Cancer Society, Pisa, 24-27 October 2004. Tumori, a Journal of Experimental and Clinical Oncology, I Supplementi, Vol. 4(2), p.72, March/April 2005, Il pensiero Scientifico Editore (IF 0.701). ISSN: 0300-8916.


• A. Micheli, A. Starita. Adaptive Processing of Complex Data Structures in QSPR/QSAR Analysis, Proceedings of the Complexity in the Living: a problem-oriented approach, International Meeting II Edition, Rome - Italy, September, 28-30, 2004, CISB - University of Rome "La Sapienza", Istituto Superiore di Sanità. pp. 206-208. Rapporti ISTISAN 05/20. © Istituto Superiore di Sanità 2005. Stampato da Tipografia Facciotti srl, Roma, Settembre 2005. ISSN 1123-3117.


• A. Micheli, A. Starita. The Recursive Neural Networks Approach to QSPR/QSAR: A Methodology Proposal, Euro-QSAR 2004 - Proceedings of the 15-th European Symposium on Quantitative Structure-Activity Relationships and Molecular Modelling, September 2004, Istanbul, Turkey - pp. 553-554, published by Computer-aided drug design & development society in Turkey, Ankara, Turkey. ISBN 975-00782-0-9.


• L. Nicotra, A. Micheli, A. Starita. Fisher Kernel for Tree Structured Data, Proceedings of IJCNN'2004-International Joint Conference on Neural Networks - Budapest, July 2004. Vol.3, pp. 1917-1922. © 2004 IEEE, ISBN: 0-7803-8359-1.


• A. Micheli, F. Portera, A. Sperduti. A Preliminary Experimental Comparison of Recursive Neural Networks and a Tree Kernel Method for QSAR/QSPR Regression Tasks, ESANN'2004, 12th European Symposium on Artificial Neural Networks - Bruges (Belgium) 28-30 April 2004. Proceedings of ESANN'2004, pp. 293-298. D-side. ISBN 2-930307-04-8.


• A. Micheli, D. Sona, A. Sperduti. Formal Determination of Context in Contextual Recursive Cascade Correlation Networks, Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, O. Kaynak and E. Alpaydin and E. Oja and L. Xu, editors. Lecture Notes in Computer Science, LNCS Vol. 2714, pp. 173-180. Springer-Verlag Berlin Heidelberg, June 2003. ISBN: 3-540-40408-2.


• A. Micheli, F. Portera, A. Sperduti. QSAR/QSPR Studies by Kernel Machines, Recursive Neural Networks and Their Integration. In: WIRN VIETRI 2003 Vietri Sul Mare (Sa), ITALY, June 4-7, 2003, Neural Nets, LNCS. Lecture Notes in Computer Science, vol. 2859 LNCS, p. 308-315, Berlin Heidelberg:Springer-Verlag, 2003, ISBN: 3-540-20227-7, ISSN: 0302-9743.


• A. Micheli, D. Sona, A. Sperduti. Recursive Cascade Correlation for Contextual Processing of Structured Data, WCCI/IJCNN'2002 - Proceedings of the World Congress on Computational Intelligence / INNS-IEEE International Joint Conference on Neural Networks. May 2002 IEEE-WCCI JCNN 2002 Vol. 1, pp. 268-273. © 2002 IEEE, ISBN 0-7803-7278-6.


• B. Hammer, A. Micheli, A. Sperduti. A General Framework for Unsupervised Processing of Structured Data, ESANN'2002, 10th European Symposium on Artificial Neural Networks - Bruges (Belgium) 24-26 April 2002. Proceedings of ESANN'2002, pp. 389-394. D-side. ISBN 2-930307-02-1.


• A. Micheli, A. Sperduti, A. Starita, A.M. Bianucci. Design of New Biologically Active Molecules by Recursive Neural Networks, IJCNN'2001 - Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Vol. 4, pp. 2732 - 2737, Washington, DC; July 2001. ISBN 0-7803-7044-9.


• A. Micheli, D. Sona, A. Sperduti. Bi-causal Recurrent Cascade Correlation, IJCNN'2000 - Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IEEE Computer Society Press), Volume: 3, 2000, pp. 3-8. ISBN 0-7695-0619-4.


• M. Hagenbuchner, A. Micheli, A. C. Tsoi. Building MLP Networks by Construction, IJCNN'2000 - Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IEEE Computer Society Press), Volume: 4, 2000, pp. 549-554. ISBN 0-7695-0619-4.


• A.M. Bianucci, A. Micheli, A. Sperduti, A. Starita. Quantitative Structure-Activity Relationships of Benzodiazepines by Recursive Cascade Correlation, IEEE Proceedings of IJCNN98- IEEE World Congress on Computational Intelligence, pp. 117-122, Anchorage, Alaska, May 1998. ISBN 0-7803-4859-1.

PhD Thesis

• A. Micheli.
  Recursive Processing of Structured Domains in Machine Learning.
  PhD Thesis, TD-13/03.
  Scuola di Dottorato "Galileo Galilei". Dipartimento di Informatica, Università di Pisa, December 2003. Servizio Editoriale Universitario di Pisa.
  (Menzione d'onore al premio AI*IA 2004 per Neodottori di ricerca, cfr. Intelligenza Artificiale, rivista dell' Associazione Italiana per l'Intelligenza Artificiale, Anno I - num. 4, Dicembre 2004, pag. 3).

Other Works and Technical Report (selection)

• C. Gallicchio, A. Micheli, P. Barsocchi, S. Chessa. Reservoir Computing Forecasting of User Movements from RSS Mote-Class Sensors Measurements, Technical Report TR-11-03, 23 November 2011, Dipartimento di Informatica - University of Pisa.


• D. Bacciu, A. Micheli, A. Sperduti. A Bottom-up Hidden Tree Markov Model, Technical Report TR-10-08, 30 April 2010, Dipartimento di Informatica - University of Pisa.


• M. R. Tiné, C.G. Bertinetto, C. Duce, A. Micheli, R. Solaro. QSAR prediction of physical, chemical and biological properties from molecular structures by recursive neural networks. XXXII AICAT 2010,26-28 May 2010, Trieste Italy pp. 104-109.


• C. Gallicchio, A. Micheli. On the Predictive Effects of Markovian and Architectural Factors of Echo State Networks. Technical Report TR-09-22, 23 November 2009, Dipartimento di Informatica - University of Pisa.


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, M. R. Tiné. Predizione tossicologica attraverso il trattamento adattativo di rappresentazioni molecolari strutturate, Capitolo nel volume Innovazione chimica per l'applicazione del REACH, SCI, Milano 2009, pp. 54-58. Edizione SCI, Roma.


• C.G. Bertinetto, C. Duce, A. Micheli, R. Solaro, M. R. Tiné. Prediction of toxicity by adaptive processing of structured molecular representations. Riassunto di comunicazione al congresso nazionale: XXIII Congresso Nazionale della Società Chimica Italiana, Sorrento (NA), Luglio 2009, pag. 70. Edizioni Ziino - Giugno 2009.


• L. Nicotra, A. Micheli, A. Starita. A Comparative Study of Tree Generative Kernels for Gene Function Prediction, Technical Report TR-07-15, Dipartimento di Informatica, Università di Pisa, July 2007.


• C. Bertinetto, C. Duce, A. Micheli, R. Solaro, A. Starita, M.R. Tiné. Prediction of the Glass Transition Temperature of Ring-Containing Polymers by Recursive Neural Network. MEDICTA 2007 8th Mediterranean Conference on Calorimetry and Thermal Analysis, Book of Abstracts P48, September 2007, Palermo Italy.


• D. Bacciu, A. Micheli, A. Starita. Feature-wise competitive repetition suppression learning for gene data clustering and feature ranking, Technical Report TR-07-04, Dipartimento di Informatica, Università di Pisa, February 2007.


• R. Bini, C. Chiappe, C. Duce, A. Micheli, R. Solaro, A. Starita, M. R. Tiné Predicting physical chemical properties of ionic liquids by RecNN, IX Congresso INCA (Interuniversity National Consortium "Chemistry for the Environment"), p.O-8, Pisa, 1-2 Marzo 2007, Publisher: SEU, Via Curtatone e Montanara 6, Pisa, Febbraio 2007.


• C. Bertinetto, C. Duce, R. Solaro, M.R.Tiné, A. Micheli, A. Starita. Application of QSPR Analysis by Neural Networks for Structures to Polymers Containing Cyclic Moieties, XXVIII National Conference on Calorimetry, Thermal Analysis and Chemical Thermodynamics, Milan, Italy, p. 13, December 11-15, 2006


• R. Bini, C. Chiappe, C. Duce, A. Micheli, A. Starita, M.R. Tiné. Predicting Melting Points of Ionic Liquids by Neural Networks for Structures, Book of abstracts: "Green Solvents for Processes", Lake Constance Friedrichshafen, Germany 8 - 11 October 2006, p. 105, DECHEMA e.V. Theodor-Heuss-Allee 25, Frankfurt am Main/Germany


• R. Bini, C. Chiappe, C. Duce, A. Micheli, A. Starita, M.R. Tiné. QSPR for Ionic Liquids by Recursive Neural Networks, XVIII IUPAC International Conference on Physical Organic Chemistry, 20-25 August 2006, Warsaw, Poland. Book of abstracts and "On-line Journal of 18th Conference on Physical Organic Chemistry", Editors: M. K. Cyranski, K. Wozniak, T.M. Krygowski, p. 53, Published August 2006, © 2006 pielaszek research. ISBN 83-89585-11-1.


• C. Duce, R. Solaro, M.R. Tiné, A. Micheli, A. Starita. Prediction of Chemical-Physical Properties by Neural Networks for Structures, Proceedings of XVII Convegno Italiano di Scienza e Tecnologia delle Macromolecole - AIM, September 2005, Napoli, Italy, pp. 290-291, Pacini Editore, Industrie Grafiche Pacini Editore S.p.A. Pisa, Agosto 2005.


• B. Hammer, A. Micheli, N. Neubauer, A. Sperduti, M. Strickert. Self-Organizing Maps for Time Series, Proceedings of 5th workshop on Self-Organizing Maps - WSOM 2005, Paris, September 2005, pp. 115-122, Ed. M.Cottrell. [Scholar google 27 citations - Jul 2014]


• C. Duce, R. Solaro, M.R. Tiné, A. Micheli, A. Starita. Predicting Glass Transition Temperature of Acrylic and Methacrylic Polymers from Monomeric Structure by Recursive Neural Networks, Congresso SCI 2004 (Società Chimica Italiana) - Sezione Toscana, vol. 1, pp. 69, Pisa, Dicembre 2004.


• V. Maggini, R. Barale, F. Canzian, A. Micheli, A.M. Rossi, A. Starita et al. Approcci di Intelligenza Computazionale per l'Analisi dei Dati di uno Studio Caso-Controllo per la Suscettibilità Genetica al Cancro Orofaringeo (HNSCC), Atti del VI Convegno FISV (Federazione Italiana Scienze della Vita)- Riva del Garda, 30 Settembre-3 Ottobre 2004, p. 441.


• L. Bernazzani, C. Duce, V. Mollica, M.R. Tiné, A. Micheli, A. Sperduti, A. Starita. Recursive Neural Networks Prediction of Thermodynamic Properties from Molecular Structures. Application to Mono- and Poly-functional Linear Molecules, 13th International Congress on Thermal Analysis and Calorimetry ICTAC 2004 Chia Laguna, Italia, September 12-19 2004, p.114, Grafiche Sainas, September 2004.


• C. Duce, R. Solaro, M.R. Tiné, A. Micheli, A. Sperduti, A. Starita. Recursive Neural Networks Prediction of Polymer Glass Transition Temperature from Monomer Structure. An Application to Acrylic and Methacrylic Polymers, 13th International Congress on Thermal Analysis and Calorimetry ICTAC 2004 Chia Laguna, Italia, September 12-19 2004, p. 52, Grafiche Sainas, September 2004.


• L. Bernazzani, C. Duce, A. Micheli, V. Mollica, A. Sperduti, A. Starita, M.R. Tiné. Predicting Thermodynamic Properties from Molecular Structures by Recursive Neural Networks. Comparison with Classical Group Contributions Methods, TR-04-16. Dipartimento di Informatica, Università di Pisa, October 2004.


• B. Hammer, P. Tino, A. Micheli. A Mathematical Characterization of the Architectural Bias of Recursive Models, Preprint no. 252 Technical report, 252- 2004 Universitat Osnabruck- Germany.


• A. Micheli, D. Sona, A. Sperduti. A Note on Formal Determination of Context in Contextual Recursive Cascade Correlation Networks, TR-04-04. , Dipartimento di Informatica, Università di Pisa, January 2004.


• L. Bernazzani, C. Duce, V. Mollica, M.R. Tiné, A. Micheli, A. Sperduti, A. Starita. Predicting Thermodynamic Properties from Molecular Structures by Recursive Neural Networks. An Extension to Polyfunctional Compounds, XVI Convegno Italiano di scienza e tecnologia delle macromolecole AIM, Pisa 22-25 September 2003, vol. 1, pp. 97-98, Pacini Editore, Industrie Grafiche Pacini Editore S.p.A. Pisa, September 2003.


• B. Hammer, A. Micheli, A. Sperduti. A General Framework for Self-Organizing Structure Processing Neural Networks, TR-03-04. , Dipartimento di Informatica, Università di Pisa, February 2003


• L. Bernazzani, C. Duce, V. Mollica, M. R. Tiné, A. Micheli, A. Sperduti, A. Starita. Predicting Thermodynamic Properties from Molecular Structures by Recursive Neural Networks, International Workshop on "Advanced Frontiers in Polymer Science" (AFPS 2002), Pisa, 12-13 September 2002, p. 62. Stampa La Grafica Pisana - Bientina (PI) - Settembre 2002.


• A. Micheli. QSAR and Drug Design by Neural Network for Structures, Lecture Notes of "Artificial Intelligence and Heuristic Methods in Bioinformatics", A NATO Advanced Studies Institute, pp. 47-48, San Miniato, Italy, October 1-11, 2001.


• A.M. Bianucci, G. Biagi, I. Giorgi, O. Livi, A. Micheli, V. Scartoni, A. Sperduti, A. Starita. A QSAR study of A1 Adenosine Receptor Antagonists by a New Neural Network Model, Proceeding of XIII Noordwijkerhout-Camerino Symposium (Trends in Drug Research), Vol. 1, 41, Noordwijkerhout , 6-11 May 2001. Eds. KNCV-EFMC.