Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
59 entries « ‹ 2 of 2
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Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
A Generative Multiset Kernel for Structured Data Conference
Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series, vol. 7552, Springer-Verlag, BERLIN HEIDELBERG, 2012.
@conference{11568_156516,
title = {A Generative Multiset Kernel for Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1007/978-3-642-33269-2_8},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {7552},
pages = {57--64},
publisher = {Springer-Verlag},
address = {BERLIN HEIDELBERG},
abstract = {The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Input-Output Hidden Markov Models for Trees Conference
ESANN 2012 - The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings, Ciaco scrl - i6doc.com, 2012.
@conference{11568_152836,
title = {Input-Output Hidden Markov Models for Trees},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {ESANN 2012 - The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Proceedings},
pages = {25--30},
publisher = {Ciaco scrl - i6doc.com},
abstract = {The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data pro- cessing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discrimina- tive structural information than non-input-driven approaches.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data pro- cessing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discrimina- tive structural information than non-input-driven approaches.
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Adaptive Tree Kernel by Multinomial Generative Topographic Mapping Conference
Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway (NJ), 2011.
@conference{11568_145907,
title = {Adaptive Tree Kernel by Multinomial Generative Topographic Mapping},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6033423&contentType=Conference+Publications&refinements%3D4294413850%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6033131%29},
doi = {10.1109/IJCNN.2011.6033423},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proceedings of the International Joint Conference on Neural Networks},
pages = {1651--1658},
publisher = {IEEE},
address = {Piscataway (NJ)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli; Paolo, Barsocchi; Stefano, Chessa
Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing Conference
Proceedings of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI), 2011.
@conference{11568_202140,
title = {Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing},
author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Barsocchi Paolo and Chessa Stefano},
url = {http://ijcai-11.iiia.csic.es/files/proceedings/Space,%20Time%20and%20Ambient%20Intelligence%20Proceeding.pdf},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proceedings of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI)},
pages = {1--6},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
A Bottom-up Hidden Tree Markov Model Technical Report
Università di Pisa no. TR-10-08, 2010.
@techreport{11568_254437,
title = {A Bottom-up Hidden Tree Markov Model},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
url = {http://compass2.di.unipi.it/TR/Files/TR-10-08.pdf.gz},
year = {2010},
date = {2010-04-01},
urldate = {2010-04-01},
volume = {TR-10-08},
number = {TR-10-08},
pages = {1--22},
institution = {Università di Pisa},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Bottom-Up Generative Modeling of Tree-Structured Data Conference
LNCS 6443: Neural Information Processing. Theory and Algorithms. Part I, vol. 6443, Springer-Verlag, BERLIN HEIDELBERG, 2010.
@conference{11568_142187,
title = {Bottom-Up Generative Modeling of Tree-Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1007/978-3-642-17537-4_80},
year = {2010},
date = {2010-01-01},
booktitle = {LNCS 6443: Neural Information Processing. Theory and Algorithms. Part I},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {6443},
pages = {660--668},
publisher = {Springer-Verlag},
address = {BERLIN HEIDELBERG},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti
Compositional Generative Mapping of Structured Data Conference
Proceedings of the 2010 IEEE InternationalJoint Conference on Neural Networks(IJCNN'10), IEEE, 2010.
@conference{11568_136433,
title = {Compositional Generative Mapping of Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1109/IJCNN.2010.5596606},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
booktitle = {Proceedings of the 2010 IEEE InternationalJoint Conference on Neural Networks(IJCNN'10)},
pages = {1359--1366},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Davide, BACCIU; Alessio, BOTTA; Dan, STEFANESCU
A framework for semantic querying of distributed data-graphs via information granules Conference
Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control, ACTA PRESS, Anaheim, CA, USA, 2007.
@conference{11568_466672,
title = {A framework for semantic querying of distributed data-graphs via information granules},
author = {BACCIU Davide and BOTTA Alessio and STEFANESCU Dan},
url = {http://pages.di.unipi.it/bacciu/wp-content/uploads/sites/12/2016/04/bbs_ISC07.pdf
http://dl.acm.org/citation.cfm?id=1647449.1647477},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control},
pages = {161--166},
publisher = {ACTA PRESS},
address = {Anaheim, CA, USA},
abstract = {Regular path queries (RPQ) represent a common and convenient way to access and extract knowledge represented as labeled and weighted data-graphs. In this paper, we look to enhance the information representation in data-graphs and RPQs by augmenting their expressive power with the use of semantically meaningful knowledge in the form of information granules. We extended a recent distributed algorithm for the evaluation of RPQs on spatial networks by introducing fuzzy weights in place of crisp values both in the data-graphs and the query formulation. Moreover, we describe two alternative strategies for determining the costs of the paths computed by the fuzzy RPQ evaluation process. A spatial network case-study is used to illustrate the soundness of the approach.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Regular path queries (RPQ) represent a common and convenient way to access and extract knowledge represented as labeled and weighted data-graphs. In this paper, we look to enhance the information representation in data-graphs and RPQs by augmenting their expressive power with the use of semantically meaningful knowledge in the form of information granules. We extended a recent distributed algorithm for the evaluation of RPQs on spatial networks by introducing fuzzy weights in place of crisp values both in the data-graphs and the query formulation. Moreover, we describe two alternative strategies for determining the costs of the paths computed by the fuzzy RPQ evaluation process. A spatial network case-study is used to illustrate the soundness of the approach.
Davide, BACCIU; Alessio, BOTTA; Dan, STEFANESCU
Augmenting the Distributed Evaluation of Path Queries via Information Granules Conference
Proceedings of the 5th International Workshop on Mining and Learning with Graphs (MLG'07), 2007.
@conference{11568_466673,
title = {Augmenting the Distributed Evaluation of Path Queries via Information Granules},
author = {BACCIU Davide and BOTTA Alessio and STEFANESCU Dan},
url = {http://mlg07.dsi.unifi.it/pdf/16_Botta.pdf},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the 5th International Workshop on Mining and Learning with Graphs (MLG'07)},
pages = {105--109},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
59 entries « ‹ 2 of 2
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