Computational Intelligence & Machine Learning Group
    Department of Computer Science - University of Pisa

 
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Datasets & Software

 


Description

The current research of the Group addresses the development of new Machine Learning methodologies.
Public datasets and software tools related to the research on Machine Learning recently developed by the group are lisetd below.





Software

  • Python implementation for the Contextual Graph Markov Model (CGMM): CGMM@github by F. Errica.
    Related to the paper: D. Bacciu, E. Federico, A. Micheli. Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. To Appear in the Proceedings of ICML 2018. Avalaible at arXiv:1805.10636 .


Data sets
  • ARTREE (tree data set) - Artificial Tree Generator: ARTREE page @ CIML
    Related to the paper: 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, no.12, pp.1987-2002, Dec. 2012

  • Alkanes Dataset (tree data set): Alkanes @ CIML
    Related to the paper: 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.

  • Indoor User Movement Prediction from RSS data Data Set (sequences): "Indoor" Data Set @ UCI Machine Learning Repository
    Related to the paper: D. Bacciu, P. Barsocchi, S. Chessa, C. Gallicchio, and A. Micheli, 'An experimental characterization of reservoir computing in ambient assisted living applications', Neural Computing and Applications, Springer-Verlag, vol. 24 (6), pp. 1451-1464, ISSN 0941-0643, 2014.

  • Activity Recognition system based on Multisensor data fusion (AReM) (sequences): "AReM" Data Set @ UCI Machine Learning Repository
    Related to the paper: F. Palumbo, C. Gallicchio, R. Pucci and A. Micheli, 'Human activity recognition using multisensor data fusion based on Reservoir Computing', Journal of Ambient Intelligence and Smart Environments, 2016, 8 (2), pp. 87-107.

  • Balance Assessment Dataset (sequences): BalanceDataset @ CIML
    Related to the paper: 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, vol. 66 (2017), pp. 60-74.



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